Pith. sign in

REVIEW 5 major objections 5 minor 1 cited by

Cloud Platforms for Developing Generative AI Solutions: A Scoping Review of Tools and Services

T0 review · 5 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A scoping review argues that major cloud platforms offer converging but differently positioned toolchains for generative AI, and that the right choice depends on workload-specific needs.

desk verdict A broad but sloppy scoping review of cloud platforms for generative AI: useful as a first orientation, but the comparative tables contain verifiable factual errors and the evidence is heavily vendor-sourced, so it needs major revision before anyone should rely on it. read the letter →

arxiv 2412.06044 v1 pith:X75G6XYW submitted 2024-12-08 cs.DC cs.AIcs.CY

Dhavalkumar Patel , Ganesh Raut , Satya Narayan Cheetirala , Girish N Nadkarni , Robert Freeman , Benjamin S. Glicksberg , Eyal Klang , Prem Timsina This is my paper · ORCID
classification cs.DCcs.AIcs.CY
keywords GenerativeAICloudcomputingScopingreviewHigh-performanceServerlessarchitecturesEdgeVendorcomparisonsecurity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a scoping review of cloud platforms for building generative AI applications. It argues that AWS, Microsoft Azure, Google Cloud, IBM, Oracle, and Alibaba Cloud each assemble a full generative-AI stack, covering compute, serverless, edge, storage, data, AI services, security, and cost, but position those pieces differently. The review claims Azure currently leads managed AI services through its OpenAI partnership, while AWS and Google Cloud lead in raw infrastructure, and IBM leads in responsible and enterprise AI; Oracle and Alibaba target specific enterprise and regional niches. If the review is right, an organization's cloud choice matters less at the level of basic capability and more at the level of fit, meaning which provider's strengths align with its scale, compliance, and cost constraints.

What carries the argument

The carrying mechanism is a comparative framework that evaluates providers along eight dimensions: high-performance computing and GPUs, serverless architectures, edge computing, storage and data lakes or warehousing, generative-AI development ecosystems, API-accessible AI services, security, and cost. Named in the paper as the 'comparative framework,' it organizes service matrices, capability tables, and SWOT analyses for AWS, Azure, Google Cloud, IBM, Oracle, and Alibaba Cloud into a single decision-oriented map. The framework's work is to turn a large collection of vendor offerings into comparable columns so that strengths and weaknesses can be weighed side by side, while the review uses Arksey and O'Malley's scoping-review methodology to bound the literature search.

What would settle it

A standardized benchmark, running the same generative-AI model, dataset, and request pattern on AWS, Azure, Google Cloud, IBM, Oracle, and Alibaba with independent measurement of cost, latency, and throughput, would confirm or overturn claims such as Azure's lead in managed AI services and Google's TPU performance advantages.

Watch

Extended reading notes

Core claim

The paper's central claim is that the cloud ecosystem for generative AI can be systematically mapped and compared across service categories, and that this mapping reveals genuine, decision-relevant differences among providers. On the paper's own terms, the discovery is that every major provider now offers an end-to-end generative-AI stack, so competition has shifted from availability to emphasis: Azure's exclusive OpenAI partnership gives it the lead in managed AI services; AWS and Google Cloud dominate scalability, infrastructure, orchestration, and security; IBM leads in ethical and explainable AI; and Oracle excels in enterprise integration. The review presents these as comparative findings supported by service matrices, SWOT analyses, and market-share data, and it recommends that enterprises match provider strengths to their own needs, use hybrid or multi-cloud strategies to reduce lock-in, and conduct their own total-cost-of-ownership analysis before choosing.

Load-bearing premise

The review's comparative conclusions assume that the vendor documentation, marketing pages, and industry web resources that make up a majority of its 255 references are accurate, unbiased, and current enough to support judgments about which provider leads.

Editorial extensions

If this is right

  • Enterprises can use the paper's service matrices to shortlist cloud providers by the specific needs of a generative-AI project rather than by general reputation.
  • Azure's exclusive partnership with OpenAI implies that teams wanting frontier GPT models through a managed API will most often land on Azure, while teams wanting custom training at scale may start with AWS or Google Cloud.
  • The paper's recommendation of hybrid and multi-cloud strategies implies that vendor lock-in is best treated as a design constraint from the start, not as a later migration problem.
  • Because cost models differ by usage pattern, the paper's cost analysis says organizations should run their own total-cost-of-ownership study before committing to a provider.
  • Security, compliance, and explainability tools now exist across all major providers, so the differentiator is which provider's governance features match an organization's regulatory environment.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because most references are vendor web pages and provider documentation, the 'leadership' claims, such as Azure's managed-AI lead, are closer to vendor positioning than to independent measurement; neutral benchmarks would be needed to confirm them.
  • The review's structure implies a testable extension: run the same generative-AI workload, with the same model, data, and traffic pattern, on all six providers and compare latency, throughput, and cost, which would turn the comparative map into a quantitative ranking.
  • The inclusion of Databricks, Snowflake, and the 'Modern AI Stack' suggests the unit of comparison may soon shift from cloud provider to toolchain, with providers becoming one layer among GPU, vector database, orchestration, and observability vendors.
  • One consequence the authors leave implicit is that specific service names in the tables will date quickly, while the comparative dimensions themselves will remain useful for future updates.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. This paper presents a scoping review of cloud platforms for developing generative AI solutions, covering AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud, and Alibaba Cloud, with additional sections on Databricks and Snowflake. The authors aim to critically analyze and compare provider offerings across compute, serverless, edge, storage, data, security, cost, and AI-specific services, and to provide practitioner guidance. The manuscript includes extensive tables, figures, supplementary material, SWOT analyses, and a bibliometric analysis of 255 references. The central claim is that the review provides a reliable comparative guide to selecting cloud providers for generative AI.

Significance. If the comparative content were accurate, the paper would be a useful broad map of the current cloud AI landscape for practitioners and researchers. The authors cover an unusually wide set of providers and service categories, and the supplementary matrices, SWOT analyses, and reference-distribution analysis are helpful organizational devices. The paper does not present new empirical measurements, but it could serve as a starting point for provider selection if the factual basis is repaired. However, the paper's value as a 'critical' comparative review depends on the correctness of its central tables and the independence of its sources; in its present form that value is substantially compromised by the errors and citation problems identified below.

major comments (5)
  1. [Table 3, §4.1.1] Table 3 lists AWS EC2 P5 instances and Google Cloud A3 instances as each having 16 NVIDIA H100 GPUs; the official AWS and Google documentation specifies 8 H100 GPUs per instance for both. The accompanying text in Section 4.1.1 then concludes that 'AWS and Google Cloud lead in raw GPU capacity with their latest offerings supporting up to 16 NVIDIA H100 GPUs per instance,' so this error directly supports a load-bearing comparative claim. The authors should correct Table 3 and re-check all GPU counts in Tables 2 and 3 against vendor documentation.
  2. [Section 2.2.2] Section 2.2.2 contains literal question marks in place of citation numbers in all five bullet points (e.g., 'cost-effectiveness ?', 'AWS EC2 P4d instances ?', 'Amazon SageMaker Autopilot ?'). A scoping review that describes key cloud capabilities for generative AI cannot leave its central claims uncited; this indicates an incomplete reference pass and needs to be fixed systematically. The Section 1.3 methodology promises a 'systematic and rigorous analytical approach,' which these placeholders contradict.
  3. [Sections 5.2, 5.3, 4.5.6, 4.5.7, Tables 8 and 9] Sections 5.2 and 5.3 are near-verbatim duplicates ('Security Aspects' and 'Deployment and Orchestration Challenges' contain the same text with different reference numbers), and Tables 8 and 9 are identical duplicated tables with different captions. In addition, Section 4.5.6 is titled 'Monitoring and Observability' but its content discusses interoperability and ethical AI, while Section 4.5.7 is titled 'Challenges and Future Directions' but discusses API-accessible services. These structural problems make the manuscript internally inconsistent and must be resolved before the review can be considered reliable.
  4. [Section 4.5.4] In Section 4.5.4, the claims about serverless inference and model compression are supported by references [7] and [8]; [7] is an image-captioning paper and [8] is the Arksey and O'Malley scoping-review methodology paper, neither of which supports the statements. Similar citation mismatches appear elsewhere (e.g., reference [54] is labeled 'Azure AI' but points to a Google Cloud URL). Because the paper's comparative claims are only as credible as its citations, the authors need to conduct a full citation audit.
  5. [Sections 3.2.2 and S5.1] The paper's comparative conclusions depend heavily on provider-authored or vendor-adjacent sources: Section S5.1 reports that 56.1% of the 255 references are web resources and only 5.5% are cloud provider documentation, with vendor blogs counted inside the web category. A central example is the claim in Section 3.2.2 that Azure's 'exclusive partnership with OpenAI' allows it to 'take the lead among managed AI services,' which is supported by Microsoft's own Azure OpenAI page. For a review that promises to 'critically analyze' cloud platforms, the authors must either add independent third-party benchmarks and analyst assessments or substantially qualify conclusions that are based on vendor marketing material.
minor comments (5)
  1. [Section 1.2] The paragraph in Section 1.2 repeats the same statement about 60% of enterprises planning to integrate generative AI by 2025 twice in consecutive sentences; one occurrence should be removed.
  2. [Tables 2 and 6] Table 2's 'Max GPU per Instance' column mixes different instance families (P4d, P3, G5) under one entry, which is misleading; Table 6 reports 'Max Throughput' values such as 50 Gbps for Azure Blob and 240 Gbps for Google Cloud Storage without clear definitions or sourcing. These tables need per-instance detail and cited units.
  3. [Figures and headings] There are numerous typographical issues: 'T able' appears in table captions, 'F uture' appears in headings, 'Iransformer' appears in Figure 2, 'GAteway' appears in Figure 10, and Section 5.1 is titled 'Performance Analysis' but begins with text about security concerns. These should be corrected in a careful copyedit.
  4. [Section 3.2.2] The first paragraph of Section 3.2.2 contains a duplicated sentence fragment ('Microsoft Azure is the second-largest cloud provider, renowned for its enterprise-friendly environment and deep Microsoft Azure is the second-largest...'), which needs to be rewritten.
  5. [Reference list] Reference [54] is labeled 'Azure AI' but points to a Google Cloud URL, and several web references in the bibliography lack access dates; the reference list should be standardized.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a descriptive scoping review with no fitted parameters, derived predictions, or self-citation chain; source bias and factual errors are correctness concerns, not circularity.

full rationale

This manuscript does not present a formal derivation, predictive model, or fitted parameter that could reduce to its own inputs. Its central claim is a comparative synthesis of cloud services for generative AI, assembled from cited vendor documentation, web resources, and academic literature. No equation or quantitative prediction is derived from a fitted quantity, and no result is justified by a self-citation: the reference list contains no works by the authors. Statements such as Azure's 'exclusive partnership with OpenAI' or claims about provider strengths are supported by external (mostly vendor-authored) citations, which may be biased or inaccurate, but this is a matter of evidence quality and verification rather than circularity in the derivation chain. The manuscript even contains missing-citation placeholders ('?') and verifiable factual errors in comparative tables (e.g., Table 3 listing 16 H100 GPUs for AWS P5 and Google A3), but those are internal consistency or correctness defects. Circularity requires a specific reduction of a claimed result to its defining input or to a self-citation that carries the argument; no such reduction is present. The appropriate finding is therefore no significant circularity, with a score of 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The review introduces no free parameters or invented entities. Its conclusions rest entirely on the completeness and reliability of its cited literature, which is dominated by web resources and vendor documentation. This creates a dependency on the source base rather than on any original data.

assumptions (3)
  • domain assumption The selected databases and grey literature provide comprehensive coverage of cloud offerings for generative AI.
    Section 1.3 asserts a broad search across IEEE Xplore, ACM, Google Scholar, and provider documentation, but no search strings or coverage dates are given.
  • domain assumption Vendor documentation and web sources are reliable, unbiased evidence for comparing provider capabilities.
    Sections 3 and 4 rely heavily on vendor-authored pages, such as AWS, Azure, and Google Cloud blogs, to characterize strengths and weaknesses.
  • domain assumption The Arksey and O'Malley scoping review framework was applied systematically.
    Section 1.3 cites Arksey and O'Malley but does not report the standard stages, such as identification, screening, charting, and collating, in a reproducible way.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Cloud Platforms for Developing Generative AI Solutions: A Scoping Review of Tools and Services." pith.science (2026). https://pith.science/paper/X75G6XYW

@misc{pith2026241206044,
  author       = {Pith},
  title        = {Pith review of: Cloud Platforms for Developing Generative AI Solutions: A Scoping Review of Tools and Services},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X75G6XYW}},
  note         = {Machine review of arXiv:2412.06044}
}
read the original abstract

Generative AI is transforming enterprise application development by enabling machines to create content, code, and designs. These models, however, demand substantial computational power and data management. Cloud computing addresses these needs by offering infrastructure to train, deploy, and scale generative AI models. This review examines cloud services for generative AI, focusing on key providers like Amazon Web Services (AWS), Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud, and Alibaba Cloud. It compares their strengths, weaknesses, and impact on enterprise growth. We explore the role of high-performance computing (HPC), serverless architectures, edge computing, and storage in supporting generative AI. We also highlight the significance of data management, networking, and AI-specific tools in building and deploying these models. Additionally, the review addresses security concerns, including data privacy, compliance, and AI model protection. It assesses the performance and cost efficiency of various cloud providers and presents case studies from healthcare, finance, and entertainment. We conclude by discussing challenges and future directions, such as technical hurdles, vendor lock-in, sustainability, and regulatory issues. Put together, this work can serve as a guide for practitioners and researchers looking to adopt cloud-based generative AI solutions, serving as a valuable guide to navigating the intricacies of this evolving field.

Figures

Figures reproduced from arXiv: 2412.06044 by the authors.

Figure 1
Figure 1. Structured guide through cloud-based generative AI development landscape [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Major Milestone Timeline in AI Development [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Evolution of Cloud Service Models for AI Development [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Modern AI Stack: The Emerging Building Blocks for GENAI [PITH_FULL_IMAGE:figures/full_fig_p017_4.png]
Figure 5
Figure 5. Figure 5: Architecture of distributed training for generative AI system [PITH_FULL_IMAGE:figures/full_fig_p021_5.png]
Figure 6
Figure 6. Figure 6: Architecture of serverless pipeline AI Pipeline [PITH_FULL_IMAGE:figures/full_fig_p024_6.png]
Figure 7
Figure 7. Figure 7: Architecture of a typical edge-based generative AI system [PITH_FULL_IMAGE:figures/full_fig_p027_7.png]
Figure 8
Figure 8. Figure 8: Architecture of a modern data lake optimized for generative AI workloads [PITH_FULL_IMAGE:figures/full_fig_p031_8.png]
Figure 9
Figure 9. Figure 9: Model lifecycle management process In [PITH_FULL_IMAGE:figures/full_fig_p035_9.png]
Figure 10
Figure 10. Figure 10: Performance Optimization and Scalability Best Practices for Integrating API-Accessible AI [PITH_FULL_IMAGE:figures/full_fig_p041_10.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 5 citations worldwide. Full citation record

  1. Less Context, Same Performance: A RAG Framework for Resource-Efficient LLM-Based Clinical NLP

    cs.CL 2025-05 conditional novelty 5.0 of 10

    RAG-based retrieval of 4,000 words matched whole-note LLM classification on post-operative complication AUROC at a fraction of the token cost, but equivalence is claimed from non-significant p-values rather than a pro...

Reference graph

Works this paper leans on

288 extracted references · 46 canonical work pages · cited by 1 Pith paper

  1. [7]

    Z. Shi, X. Zhou, X. Qiu, and X. Zhu, ”Improving Image Captioning with Better Use of Captions,” p. arXiv:2006.11807doi: 10.48550/arXiv.2006.11807

    arXiv doi:10.48550/arxiv.2006.11807 2006

  2. [8]

    Arksey and L

    H. Arksey and L. O 'Malley, ”Scoping studies: towards a methodological framework,” Interna- tional Journal of Social Research Methodology, vol. 8, no. 1, pp. 19-32, 2005/02/01 2005, doi: 10.1080/1364557032000119616

    doi:10.1080/1364557032000119616 2005

  3. [54]

    M. Azure. ”Azure AI.” https://cloud.google.com/ai/generative-ai (accessed

  4. [1]

    M. U. Hadi et al. , ”Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects”, Authorea Preprints, 2023

    2023

  5. [2]

    T. B. Brownet al., ”Language Models are Few-Shot Learners,” p. arXiv:2005.14165doi: 10.48550/arXiv.2005.14165

    arXiv doi:10.48550/arxiv.2005.14165 2005

  6. [3]

    Achiam et al

    J. Achiam et al. , ”Gpt-4 technical report,” arXiv preprint arXiv:2303.08774, 2023

    arXiv 2023

  7. [4]

    Team et al., ”Gemini: A Family of Highly Capable Multimodal Models”, p

    G. Team et al., ”Gemini: A Family of Highly Capable Multimodal Models”, p. arXiv:2312.11805doi: 10.48550/arXiv.2312.11805

    arXiv doi:10.48550/arxiv.2312.11805

  8. [5]

    Grand View

    R. Grand View. ”Generative AI Market Size, Share & Trends Analysis Report By Compo- nent (Service, Software), By Technology (GAN, Transformer), By End-use, By Regional Outlook, And Segment Forecasts, 2023 - 2030.” https://www.grandviewresearch.com/industry-analysis/ generative-ai-market-report (accessed

    2023

Show all 288 references
  1. [6]

    McKinsey and Company. ”The Economic Potential of Generative AI: The Next Productivity Fron- tier.” https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier (accessed

  2. [9]

    M. A. Boden, ”Creativity and artificial intelligence,” Artificial intelligence, vol. 103, no. 1-2, pp. 347-356, 1998

    1998

  3. [10]

    LeCun, Y

    Y. LeCun, Y. Bengio, and G. Hinton, ”Deep learning,” nature, vol. 521, no. 7553, pp. 436-444, 2015

    2015

  4. [11]

    Akkus et al., ”Multimodal Deep Learning,” p

    C. Akkus et al., ”Multimodal Deep Learning,” p. arXiv:2301.04856doi: 10.48550/arXiv.2301.04856

    doi arXiv

  5. [12]

    Newell, J

    A. Newell, J. C. Shaw, and H. A. Simon, ”Report on a general problem solving program,” in IFIP congress, 1959, vol. 256: Pittsburgh, PA, p. 64

    1959

  6. [13]

    D. E. Rumelhart, G. E. Hinton, and R. J. Williams, ”Learning representations by back-propagating errors,” nature, vol. 323, no. 6088, pp. 533-536, 1986. 51

    1986

  7. [14]

    J. H. Holland, ”Genetic algorithms,” Scientific american, vol. 267, no. 1, pp. 66-73, 1992

    1992

  8. [15]

    Goodfellow et al

    I. Goodfellow et al. , ”Generative adversarial nets,” Advances in neural information processing sys- tems, vol. 27, 2014

    2014

  9. [16]

    Vaswani et al., ”Attention Is All You Need,” p

    A. Vaswani et al., ”Attention Is All You Need,” p. arXiv:1706.03762doi: 10.48550/arXiv.1706.03762

    doi arXiv

  10. [17]

    Devlin, M.-W

    J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, ”BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” p. arXiv:1810.04805doi: 10.48550/arXiv.1810.04805

    doi arXiv

  11. [18]

    Ramesh, P

    A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, ”Hierarchical Text-Conditional Image Generation with CLIP Latents,” p. arXiv:2204.06125doi: 10.48550/arXiv.2204.06125

    doi arXiv

  12. [19]

    Rombach, A

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, ”High-resolution image synthesis with latent diffusion models,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10684-10695

    2022

  13. [20]

    ”Hello GPT-4: OpenAI 's Latest Language Model.” https://openai.com/index/ hello-gpt-4o/ (accessed

    OpenAi. ”Hello GPT-4: OpenAI 's Latest Language Model.” https://openai.com/index/ hello-gpt-4o/ (accessed

  14. [21]

    Dubey et al

    A. Dubey et al. , ”The llama 3 herd of models,” arXiv preprint arXiv:2407.21783, 2024

    2024 arXiv

  15. [22]

    ”Meta LLaMA 3: Advancing Open-Source Language Models.” https://ai.meta.com/blog/ meta-llama-3/ (accessed

    Meta. ”Meta LLaMA 3: Advancing Open-Source Language Models.” https://ai.meta.com/blog/ meta-llama-3/ (accessed

  16. [23]

    Gunter et al., ”Apple intelligence foundation language models,” arXiv preprint arXiv:2407.21075, 2024

    T. Gunter et al., ”Apple intelligence foundation language models,” arXiv preprint arXiv:2407.21075, 2024

    2024

  17. [24]

    Available: https://www.datacamp

    2024, ”What is GPT-4 and Why Does it Matter?.” [Online]. Available: https://www.datacamp. com/blog/what-we-know-gpt4

    2024

  18. [25]

    ”Claude 3.5 Sonnet: Advancing Conversational AI with Ethical AI Principles.” https: //www.anthropic.com/news/claude-3-5-sonnet (accessed

    Anthropic. ”Claude 3.5 Sonnet: Advancing Conversational AI with Ethical AI Principles.” https: //www.anthropic.com/news/claude-3-5-sonnet (accessed

  19. [26]

    Team et al

    G. Team et al. , ”Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,” p. arXiv:2403.05530doi: 10.48550/arXiv.2403.05530

    doi arXiv

  20. [27]

    Touvronet al., ”Llama 2: Open Foundation and Fine-Tuned Chat Models,” p

    H. Touvronet al., ”Llama 2: Open Foundation and Fine-Tuned Chat Models,” p. arXiv:2307.09288doi: 10.48550/arXiv.2307.09288

    doi arXiv

  21. [28]

    Dubey et al., ”The Llama 3 Herd of Models,” p

    A. Dubey et al., ”The Llama 3 Herd of Models,” p. arXiv:2407.21783doi: 10.48550/arXiv.2407.21783

    doi arXiv

  22. [29]

    Sharir, B

    O. Sharir, B. Peleg, and Y. Shoham, ”The cost of training nlp models: A concise overview,” arXiv 52 preprint arXiv:2004.08900, 2020

    2004 arXiv

  23. [30]

    , ”GPT-4 Technical Report,” p

    OpenAI et al. , ”GPT-4 Technical Report,” p. arXiv:2303.08774doi: 10.48550/arXiv.2303.08774

    doi arXiv

  24. [31]

    E. J. Huet al., ”Lora: Low-rank adaptation of large language models,”arXiv preprint arXiv:2106.09685, 2021

    2021 arXiv

  25. [32]

    Houlsby et al

    N. Houlsby et al. , ”Parameter-efficient transfer learning for NLP,” in International conference on machine learning, 2019: PMLR, pp. 2790-2799

    2019

  26. [33]

    Hinton, ”Distilling the Knowledge in a Neural Network,” arXiv preprint arXiv:1503.02531, 2015

    G. Hinton, ”Distilling the Knowledge in a Neural Network,” arXiv preprint arXiv:1503.02531, 2015

    2015 arXiv

  27. [34]

    Singhal et al., ”Large language models encode clinical knowledge,”arXiv preprint arXiv:2212.13138, 2022

    K. Singhal et al., ”Large language models encode clinical knowledge,”arXiv preprint arXiv:2212.13138, 2022

    2022 arXiv

  28. [35]

    Jumper et al

    J. Jumper et al. , ”Highly accurate protein structure prediction with AlphaFold,” nature, vol. 596, no. 7873, pp. 583-589, 2021

    2021

  29. [36]

    Le Scao et al

    T. Le Scao et al. , ”Bloom: A 176b-parameter open-access multilingual language model,” 2023

    2023

  30. [37]

    Hochreiter and J

    S. Hochreiter and J. Schmidhuber, ”Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997, doi: 10.1162/neco.1997.9.8.1735

    1997 doi

  31. [38]

    Bengio, P

    Y. Bengio, P. Simard, and P. Frasconi, ”Learning long-term dependencies with gradient descent is dif- ficult,” IEEE Transactions on Neural Networks, vol. 5, no. 2, pp. 157-166, 1994, doi: 10.1109/72.279181

    1994 doi

  32. [39]

    Mission, ”RAG & Generative AI: Enhancing AI Solutions with Retrieval-Augmented Generation,”

    C. Mission, ”RAG & Generative AI: Enhancing AI Solutions with Retrieval-Augmented Generation,”

  33. [40]

    Izacard and E

    G. Izacard and E. Grave, ”Leveraging passage retrieval with generative models for open domain question answering,” arXiv preprint arXiv:2007.01282, 2020

    2007 arXiv

  34. [41]

    Nakano et al

    R. Nakano et al. , ”WebGPT: Browser-assisted question-answering with human feedback,” p. arXiv:2112.09332doi: 10.48550/arXiv.2112.09332

    doi arXiv

  35. [42]

    ”SearchGPT Prototype: Exploring the Future of AI-Driven Search.” https://openai

    OpenAi. ”SearchGPT Prototype: Exploring the Future of AI-Driven Search.” https://openai. com/index/searchgpt-prototype/ (accessed

  36. [43]

    Peng et al

    B. Peng et al. , ”Graph Retrieval-Augmented Generation: A Survey,” p. arXiv:2408.08921doi: 10.48550/arXiv.2408.08921

    doi arXiv

  37. [44]

    Sarmah, B

    B. Sarmah, B. Hall, R. Rao, S. Patel, S. Pasquali, and D. Mehta, ”HybridRAG: Integrating Knowl- edge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction,” p. 53 arXiv:2408.04948doi: 10.48550/arXiv.2408.04948

    doi arXiv

  38. [45]

    Baltruˇ saitis, C

    T. Baltruˇ saitis, C. Ahuja, and L.-P. Morency, ”Multimodal machine learning: A survey and tax- onomy,” IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 2, pp. 423-443, 2018

    2018

  39. [46]

    Radford et al., ”Learning transferable visual models from natural language supervision,” presented at the International Conference on Machine Learning, 2021

    A. Radford et al., ”Learning transferable visual models from natural language supervision,” presented at the International Conference on Machine Learning, 2021. [Online]. Available: https://openai.com/ blog/clip/

    2021

  40. [47]

    ”GPT-4V(ision) System Card,” 2023

    2023

  41. [48]

    Andreas, D

    J. Andreas, D. Klein, and S. Levine, ”Learning with latent language,”arXiv preprint arXiv:1711.00482, 2017

    2017 arXiv

  42. [49]

    Mell, ”The NIST Definition of Cloud Computing,” NIST Special Publication, pp

    P. Mell, ”The NIST Definition of Cloud Computing,” NIST Special Publication, pp. 800-145, 2011

    2011

  43. [50]

    Amazon Web

    S. Amazon Web. ”Amazon SageMaker.” https://aws.amazon.com/sagemaker/ (accessed

  44. [51]

    Microsoft

    A. Microsoft. ”Azure Machine Learning.” https://azure.microsoft.com/en-us/products/ machine-learning (accessed

  45. [52]

    C. Google. ”AI & Machine Learning Products.” https://cloud.google.com/products/ai (ac- cessed

  46. [53]

    Amazon Web

    S. Amazon Web. ”A WS Comprehend: Natural Language Processing and Text Analytics Service.” https://aws.amazon.com/comprehend/ (accessed

  47. [55]

    C. Google. ”Google Cloud Vision API: AI-Powered Image and Video Recognition.” https://cloud. google.com/vision?hl=en (accessed

  48. [56]

    N. P. Jouppi et al. , ”In-Datacenter Performance Analysis of a Tensor Processing Unit,” p. arXiv:1704.04760doi: 10.48550/arXiv.1704.04760

    doi arXiv

  49. [57]

    C. Google. ”Google Showcases Cloud TPU v4 Pods for Large Model Training.” https://cloud. google.com/blog/topics/tpus/google-showcases-cloud-tpu-v4-pods-for-large-model-training (accessed

  50. [58]

    E. F. Coutinho, F. R. de Carvalho Sousa, P. A. L. Rego, D. G. Gomes, and J. N. de Souza, ”Elasticity in cloud computing: a survey,” annals of telecommunications - annales des t´ el´ ecommunications,vol. 70, 54 no. 7, pp. 289-309, 2015/08/01 2015, doi: 10.1007/s12243-014-0450-7

    2015 doi

  51. [59]

    Aghajanyan et al., ”Scaling Laws for Generative Mixed-Modal Language Models,” p

    A. Aghajanyan et al., ”Scaling Laws for Generative Mixed-Modal Language Models,” p. arXiv:2301.03728doi: 10.48550/arXiv.2301.03728

    doi arXiv

  52. [60]

    Amazon Web

    S. Amazon Web. ”EC2 Instance Types: P4.” https://aws.amazon.com/ec2/instance-types/p4/ (accessed

  53. [61]

    Amazon Web

    S. Amazon Web. ”A WS SageMaker Autopilot: Automate Machine Learning Model Building and Deployment.” https://aws.amazon.com/sagemaker/autopilot/ (accessed

  54. [62]

    Microsoft

    A. Microsoft. ”Azure AI Services.” https://azure.microsoft.com/en-us/products/ai-services (accessed

  55. [63]

    Amazon Web

    S. Amazon Web. ”Amazon Redshift: Cloud Data Warehousing for Analytics.” https://aws. amazon.com/redshift/ (accessed

  56. [64]

    Microsoft

    A. Microsoft. ”Azure Data Lake Analytics: On-Demand Analytics Job Service.” https://azure. microsoft.com/en-us/products/data-lake-analytics/ (accessed

  57. [65]

    C. Google. ”Introducing BigQuery Omni.” https://cloud.google.com/blog/products/data-analytics/ introducing-bigquery-omni (accessed

  58. [66]

    C. Google. ”Google Cloud CDN: High-Performance Content Delivery Network.” https://cloud. google.com/cdn?hl=en (accessed

  59. [67]

    Microsoft

    A. Microsoft. ”Azure Front Door Overview: Secure and Scalable Application Delivery.” https: //learn.microsoft.com/en-us/azure/frontdoor/front-door-overview (accessed

  60. [68]

    Amazon Web

    S. Amazon Web. ”A WS Global Accelerator: Improve Availability and Performance of Global Ap- plications.” https://aws.amazon.com/global-accelerator/ (accessed

  61. [69]

    Amazon Web

    S. Amazon Web. ”A WS Global Accelerator: Key Features for Enhanced Global Application Perfor- mance.” https://aws.amazon.com/global-accelerator/features/ (accessed

  62. [70]

    Microsoft

    A. Microsoft. ”Azure Front Door: Scalable and Secure Application Delivery Network.” https: //azure.microsoft.com/en-us/products/frontdoor/ (accessed

  63. [71]

    ”Pay-As-You-Go Cloud Computing: Flexible and Cost-Effective Cloud Solutions.” https://www.digitalocean.com/resources/articles/pay-as-you-go-cloud-computing (accessed

    DigitalOcean. ”Pay-As-You-Go Cloud Computing: Flexible and Cost-Effective Cloud Solutions.” https://www.digitalocean.com/resources/articles/pay-as-you-go-cloud-computing (accessed

  64. [72]

    Amazon Web

    S. Amazon Web. ”A WS Cost Explorer: Visualize and Manage Your A WS Costs and Usage.” 55 https://aws.amazon.com/aws-cost-management/aws-cost-explorer/ (accessed

  65. [73]

    Microsoft

    A. Microsoft. ”Azure Cost Management: Optimize Your Cloud Spending and Improve Efficiency.” https://azure.microsoft.com/en-us/products/cost-management (accessed

  66. [74]

    C. Google. ”Google Cloud Cost Management: Tools to Optimize Your Cloud Spend.” https: //cloud.google.com/cost-management?hl=en (accessed

  67. [75]

    Bernstein, ”Containers and Cloud: From LXC to Docker to Kubernetes,”IEEE Cloud Computing, vol

    D. Bernstein, ”Containers and Cloud: From LXC to Docker to Kubernetes,”IEEE Cloud Computing, vol. 1, no. 3, pp. 81-84, 2014, doi: 10.1109/MCC.2014.51

    2014 doi

  68. [76]

    Baldini et al., ”Serverless Computing: Current Trends and Open Problems,” p

    I. Baldini et al., ”Serverless Computing: Current Trends and Open Problems,” p. arXiv:1706.03178doi: 10.48550/arXiv.1706.03178

    doi arXiv

  69. [77]

    Singh, A

    P. Singh, A. Kaur, and S. S. Gill, ”Machine learning for cloud, fog, edge and serverless computing environments: Comparisons, performance evaluation benchmark and future directions,” International Journal of Grid and Utility Computing, vol. 13, no. 4, pp. 447-457, 2022

    2022

  70. [78]

    Kreuzberger, N

    D. Kreuzberger, N. K¨ uhl, and S. Hirschl, ”Machine learning operations (mlops): Overview, definition, and architecture,” IEEE access, vol. 11, pp. 31866-31879, 2023

    2023

  71. [79]

    Buyya, C

    R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, ”Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009/06/01/ 2009, doi: https://do...

    2009 doi

  72. [80]

    J. Hong, T. Dreibholz, J. A. Schenkel, and J. A. Hu, ”An overview of multi-cloud computing,” inWeb, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-20...

    2019

  73. [81]

    W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, ”Edge Computing: Vision and Challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637-646, 2016, doi: 10.1109/JIOT.2016.2579198

    2016

  74. [82]

    Alhenaki, A

    L. Alhenaki, A. Alwatban, B. Alahmri, and N. Alarifi, ”Security in cloud computing: a survey,” International Journal of Computer Science and Information Security (IJCSIS), vol. 17, no. 4, pp. 67-90, 2019

    2019

  75. [83]

    Zhang, Y

    K. Zhang, Y. Mao, S. Leng, Y. He, and Y. Zhang, ”Mobile-edge computing for vehicular networks: A promising network paradigm with predictive off-loading,” IEEE vehicular technology magazine, vol. 12, no. 2, pp. 36-44, 2017. 56

    2017

  76. [84]

    X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, ”In-Edge AI: Intelligentizing Mo- bile Edge Computing, Caching and Communication by Federated Learning,” p. arXiv:1809.07857doi: 10.48550/arXiv.1809.07857

    doi arXiv

  77. [85]

    H. Li, K. Ota, and M. Dong, ”Learning IoT in edge: Deep learning for the Internet of Things with edge computing,” IEEE network, vol. 32, no. 1, pp. 96-101, 2018

    2018

  78. [86]

    Schuld, I

    M. Schuld, I. Sinayskiy, and F. Petruccione, ”An introduction to quantum machine learning,” Con- temporary Physics, vol. 56, no. 2, pp. 172-185, 2015

    2015

  79. [87]

    ”IBM Quantum: Pioneering the Future of Quantum Computing.” https://www.ibm.com/ quantum (accessed

    Ibm. ”IBM Quantum: Pioneering the Future of Quantum Computing.” https://www.ibm.com/ quantum (accessed

  80. [88]

    Microsoft

    A. Microsoft. ”Azure Quantum Computing: Solutions for Next-Generation Computing.” https: //azure.microsoft.com/en-us/solutions/quantum-computing/ (accessed

  81. [89]

    Amazon Web

    S. Amazon Web. ”A WS Quantum Computing: Tools and Services for Quantum Innovation.”https: //aws.amazon.com/products/quantum/ (accessed

  82. [90]

    ”Google Quantum AI: Advancing Quantum Computing for Real-World Impact.” https: //quantumai.google/ (accessed

    Google. ”Google Quantum AI: Advancing Quantum Computing for Real-World Impact.” https: //quantumai.google/ (accessed

  83. [91]

    Dell. ”Addressing the Environmental Footprint While Optimizing the Handprint of AI.” https:// www.dell.com/en-us/blog/addressing-the-environmental-footprint-while-optimizing-the-handprint-of-ai/ (accessed

  84. [92]

    World Economic

    F. World Economic. ”How to Optimize AI While Minimizing Your Carbon Footprint.”https://www. weforum.org/agenda/2024/01/how-to-optimize-ai-while-minimizing-your-carbon-footprint/ (accessed

    2024

  85. [93]

    C. R. N. Staff. ”Cloud Market Share in Q2: Microsoft Drops, Google Gains, A WS Remains Leader.” https://www.crn.com/news/cloud/2024/cloud-market-share-in-q2-microsoft-drops-google-gains-aws-remains-leader (accessed

    2024

  86. [94]

    ”Worldwide Cloud Infrastructure Services Market Share by Vendor.” https://www

    Statista. ”Worldwide Cloud Infrastructure Services Market Share by Vendor.” https://www. statista.com/statistics/967365/worldwide-cloud-infrastructure-services-market-share-vendor/ (accessed

  87. [95]

    CloudZero

    T. CloudZero. ”Cloud Service Providers: A Comprehensive Guide.” https://www.cloudzero.com/ blog/cloud-service-providers/ (accessed. 57

  88. [96]

    Microsoft

    A. Microsoft. ”Azure OpenAI Service.” https://azure.microsoft.com/en-us/products/ai-services/ openai-service/ (accessed

  89. [97]

    ”Anthropic Partners with Google Cloud to Scale AI Research and Development.” https: //www.anthropic.com/news/anthropic-partners-with-google-cloud (accessed

    Anthropic. ”Anthropic Partners with Google Cloud to Scale AI Research and Development.” https: //www.anthropic.com/news/anthropic-partners-with-google-cloud (accessed

  90. [98]

    ”A WS-NVIDIA Strategic Collaboration for Generative AI.” https://nvidianews.nvidia

    Nvidia. ”A WS-NVIDIA Strategic Collaboration for Generative AI.” https://nvidianews.nvidia. com/news/aws-nvidia-strategic-collaboration-for-generative-ai (accessed

  91. [99]

    Ibm. ”IBM and NASA Release Open-Source AI Model on Hugging Face for Weather and Climate Ap- plications.” https://newsroom.ibm.com/2024-09-23-ibm-and-nasa-release-open-source-ai-model-on-hugging-face-for-weather-and-climate-applications (accessed

    2024

  92. [100]

    ”Oracle and NVIDIA Announce Sovereign AI Cloud for Europe.” https://nvidianews

    Nvidia. ”Oracle and NVIDIA Announce Sovereign AI Cloud for Europe.” https://nvidianews. nvidia.com/news/oracle-nvidia-sovereign-ai (accessed

  93. [101]

    ”Alibaba Cloud Analytics Zoo: AI-Powered Analytics on Intel Architecture.” https://www

    Intel. ”Alibaba Cloud Analytics Zoo: AI-Powered Analytics on Intel Architecture.” https://www. intel.com/content/www/us/en/customer-spotlight/stories/alibaba-cloud-analytics-zoo-customer-story. html (accessed

  94. [102]

    Chowdhery et al

    A. Chowdhery et al. , ”Palm: Scaling language modeling with pathways,” Journal of Machine Learning Research, vol. 24, no. 240, pp. 1-113, 2023

    2023

  95. [103]

    Sterling, M

    T. Sterling, M. Brodowicz, and M. Anderson, High performance computing: modern systems and practices. Morgan Kaufmann, 2017

    2017

  96. [104]

    W. J. Dally, S. W. Keckler, and D. B. Kirk, ”Evolution of the graphics processing unit (GPU),” IEEE Micro, vol. 41, no. 6, pp. 42-51, 2021

    2021

  97. [105]

    Aghajanyan et al., ”Scaling laws for generative mixed-modal language models,” in International Conference on Machine Learning , 2023: PMLR, pp

    A. Aghajanyan et al., ”Scaling laws for generative mixed-modal language models,” in International Conference on Machine Learning , 2023: PMLR, pp. 265-279

    2023

  98. [106]

    Amazon Web

    S. Amazon Web. ”A WS EC2 P5 Instance Types: High-Performance Computing for Machine Learning and AI.” https://aws.amazon.com/ec2/instance-types/p5/ (accessed

  99. [107]

    Microsoft

    A. Microsoft. ”Azure ND H100v5 Series: High-Performance GPU-Accelerated Virtual Ma- chines for AI and ML.” https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/ gpu-accelerated/ndh100v5-series?tabs=sizebasic (accessed

  100. [108]

    C. Google. ”Google Cloud On-Demand A3 VMs: Flexible and High-Performance Virtual Machines for AI Workloads.” https://cloud.google.com/skus/sku-groups/on-demand-a3-vms (accessed. 58

  101. [109]

    Amazon Web

    S. Amazon Web. ”A WS Trainium: High-Performance Machine Learning Training Chips.” https: //aws.amazon.com/machine-learning/trainium/ (accessed

  102. [110]

    C. Google. ”Google Cloud TPU v5e Documentation: High-Performance AI Accelerators for Ma- chine Learning.” https://cloud.google.com/tpu/docs/v5e (accessed

  103. [111]

    Patterson et al

    D. Patterson et al. , ”Carbon emissions and large neural network training,” arXiv preprint arXiv:2104.10350, 2021

    2021 arXiv

  104. [112]

    ”Amazon Cloud Sustainability: Reducing Environmental Impact through the Cloud.” https://sustainability.aboutamazon.com/products-services/the-cloud (accessed

    Amazon. ”Amazon Cloud Sustainability: Reducing Environmental Impact through the Cloud.” https://sustainability.aboutamazon.com/products-services/the-cloud (accessed

  105. [113]

    Chahal, M

    D. Chahal, M. Ramesh, R. Ojha, and R. Singhal, ”High performance serverless architecture for deep learning workflows,” in 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid) , 2021: IEEE, pp. 790-796

    2021

  106. [114]

    Paraskevoulakou and D

    E. Paraskevoulakou and D. Kyriazis, ”ML-FaaS: Toward Exploiting the Serverless Paradigm to Facilitate Machine Learning Functions as a Service,” IEEE Transactions on Network and Service Man- agement, vol. 20, no. 3, pp. 2110-2123, 2023

    2023

  107. [115]

    Kojs, ”A Survey of Serverless Machine Learning Model Inference,” p

    K. Kojs, ”A Survey of Serverless Machine Learning Model Inference,” p. arXiv:2311.13587doi: 10.48550/arXiv.2311.13587

    doi arXiv

  108. [116]

    Ishakian, V

    V. Ishakian, V. Muthusamy, and A. Slominski, ”Serving deep learning models in a serverless platform,” in 2018 IEEE International conference on cloud engineering (IC2E) , 2018: IEEE, pp. 257- 262

    2018

  109. [117]

    Gallego, U

    A. Gallego, U. Odyurt, Y. Cheng, Y. Wang, and Z. Zhao, ”Machine Learning Inference on Serverless Platforms Using Model Decomposition,” in Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing , 2023, pp. 1-6

    2023

  110. [118]

    M. S. Aslanpour et al. , ”Serverless edge computing: vision and challenges,” in Proceedings of the 2021 Australasian computer science week multiconference , 2021, pp. 1-10

    2021

  111. [119]

    Fu et al

    Y. Fu et al. , ” {ServerlessLLM}:{Low-Latency} Serverless Inference for Large Language Models,” in 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24) , 2024, pp. 135-153

    2024

  112. [120]

    Donati, M

    O. Donati, M. Macario, and M. H. Karim, ”Event-Driven AI Workflows in Serverless Computing: Enabling Real-Time Data Processing and Decision-Making,” 2024. 59

    2024

  113. [121]

    Arjona, P

    A. Arjona, P. G. L´ opez, J. Samp´ e, A. Slominski, and L. Villard, ”Triggerflow: Trigger-based orchestration of serverless workflows,” Future Generation Computer Systems, vol. 124, pp. 215-229, 2021/11/01/ 2021, doi: https://doi.org/10.1016/j.future.2021.06.004

    2021 doi

  114. [122]

    Loconte, S

    D. Loconte, S. Ieva, A. Pinto, G. Loseto, F. Scioscia, and M. Ruta, ”Expanding the cloud-to-edge continuum to the IoT in serverless federated learning,” Future Generation Computer Systems, vol. 155, pp. 447-462, 2024/06/01/ 2024, doi: https://doi.org/10.1016/j.future.2024.02.024

    2024 doi

  115. [123]

    S. Qi, K. Ramakrishnan, and M. Lee, ”LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning,” Proceedings of Machine Learning and Systems, vol. 6, pp. 408-425, 2024

    2024

  116. [124]

    Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, ”Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing,” Proceedings of the IEEE, vol. 107, no. 8, pp. 1738-1762, 2019, doi: 10.1109/JPROC.2019.2918951

    2019

  117. [125]

    Teerapittayanon, B

    S. Teerapittayanon, B. McDanel, and H.-T. Kung, ”Distributed deep neural networks over the cloud, the edge and end devices,” in 2017 IEEE 37th international conference on distributed computing systems (ICDCS) , 2017: IEEE, pp. 328-339

    2017

  118. [126]

    T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, ”Federated learning: Challenges, methods, and future directions,” IEEE signal processing magazine, vol. 37, no. 3, pp. 50-60, 2020

    2020

  119. [127]

    Liang, J

    T. Liang, J. Glossner, L. Wang, S. Shi, and X. Zhang, ”Pruning and quantization for deep neural network acceleration: A survey,” Neurocomputing, vol. 461, pp. 370-403, 2021

    2021

  120. [128]

    Kuzmin, M

    A. Kuzmin, M. Nagel, M. Van Baalen, A. Behboodi, and T. Blankevoort, ”Pruning vs quantization: which is better?,” Advances in neural information processing systems, vol. 36, 2024

    2024

  121. [129]

    Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, ”A survey on mobile edge computing: The communication perspective,” IEEE communications surveys & tutorials, vol. 19, no. 4, pp. 2322-2358, 2017

    2017

  122. [130]

    Gholami et al

    A. Gholami et al. , ”Squeezenext: Hardware-aware neural network design,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2018, pp. 1638-1647

    2018

  123. [131]

    Jia et al

    Y. Jia et al. , ”Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge Computing Environment,” ACM Transactions on Sensor Net- works, 2024

    2024

  124. [132]

    Patel et al

    P. Patel et al. , ”Splitwise: Efficient generative llm inference using phase splitting,” in 2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA) , 2024: IEEE, pp. 60 118-132

    2024

  125. [133]

    Y. Long, I. Chakraborty, G. Srinivasan, and K. Roy, ”Complexity-aware adaptive training and infer- ence for edge-cloud distributed AI systems,” in 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS) , 2021: IEEE, pp. 573-583

    2021

  126. [134]

    Nayak, R

    S. Nayak, R. Patgiri, L. Waikhom, and A. Ahmed, ”A review on edge analytics: Issues, challenges, opportunities, promises, future directions, and applications,” Digital Communications and Networks, vol. 10, no. 3, pp. 783-804, 2024/06/01/ 2024, doi: https://doi.org/10.1016/j.dc...

    2024 doi

  127. [135]

    Forno, A

    E. Forno, A. Spitale, E. Macii, and G. Urgese, ”Configuring an embedded neuromorphic copro- cessor using a risc-v chip for enabling edge computing applications,” in 2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) , 2021: IEEE, pp. 328-332

    2021

  128. [136]

    P. Madduru, ”Artificial Intelligence as a service in distributed multi access edge computing on 5G extracting data using IoT and including AR/VR for real-time reporting,” Information Technology In Industry, vol. 9, no. 1, pp. 912-931, 2021

    2021

  129. [137]

    Shen et al., ”Large language models empowered autonomous edge AI for connected intelligence,” IEEE Communications Magazine, 2024

    Y. Shen et al., ”Large language models empowered autonomous edge AI for connected intelligence,” IEEE Communications Magazine, 2024

    2024

  130. [138]

    Y. Tian et al., ”An Edge-Cloud Collaboration Framework for Generative AI Service Provision with Synergetic Big Cloud Model and Small Edge Models,” arXiv preprint arXiv:2401.01666, 2024

    2024 arXiv

  131. [139]

    De Prisco, B

    R. De Prisco, B. Lampson, and N. Lynch, ”Revisiting the Paxos algorithm,” Theoretical Computer Science, vol. 243, no. 1-2, pp. 35-91, 2000

    2000

  132. [140]

    Chang et al

    F. Chang et al. , ”Bigtable: A distributed storage system for structured data,” ACM Transactions on Computer Systems (TOCS), vol. 26, no. 2, pp. 1-26, 2008

    2008

  133. [141]

    H. Fang, ”Managing data lakes in big data era: What 's a data lake and why has it became popular in data management ecosystem,” in 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) , 2015: IEEE, pp. 820-824

    2015

  134. [142]

    Singhal, Data warehousing and data mining techniques for cyber security

    A. Singhal, Data warehousing and data mining techniques for cyber security . Springer Science & Business Media, 2007

    2007

  135. [143]

    Shvachko, H

    K. Shvachko, H. Kuang, S. Radia, and R. Chansler, ”The hadoop distributed file system,” in 2010 IEEE 26th symposium on mass storage systems and technologies (MSST) , 2010: Ieee, pp. 1-10

    2010

  136. [144]

    Chen and J

    B. Chen and J. Zhang, ”Content-aware scalable deep compressed sensing,” IEEE Transactions on 61 Image Processing, vol. 31, pp. 5412-5426, 2022

    2022

  137. [145]

    ”NVIDIA GPUDirect Storage: Accelerating Data Movement for GPU Computing.” https: //developer.nvidia.com/gpudirect-storage (accessed

    Nvidia. ”NVIDIA GPUDirect Storage: Accelerating Data Movement for GPU Computing.” https: //developer.nvidia.com/gpudirect-storage (accessed

  138. [146]

    Q. D. Tran, ”Toward a serverless data pipeline.”

  139. [147]

    L’heureux, K

    A. L’heureux, K. Grolinger, H. F. Elyamany, and M. A. Capretz, ”Machine learning with big data: Challenges and approaches,” Ieee Access, vol. 5, pp. 7776-7797, 2017

    2017

  140. [148]

    Olesen-Bagneux, The Enterprise Data Catalog

    O. Olesen-Bagneux, The Enterprise Data Catalog . ” O 'Reilly Media, Inc.”, 2023

    2023

  141. [149]

    Google, ”Data Analytics Innovations to Fuel AI Initiatives,” 2024/10/06 2024

    C. Google, ”Data Analytics Innovations to Fuel AI Initiatives,” 2024/10/06 2024. [Online]. Avail- able: https://cloud.google.com/blog/products/data-analytics/data-analytics-innovations-to-fuel-ai-initiatives

    2024

  142. [150]

    [Online]

    Oracle, ”Machine Learning Platform on Oracle Autonomous Data Warehouse,” 2024/10/06 2024. [Online]. Available: https://docs.oracle.com/en/solutions/ml-platform-on-adw/index.html

    2024

  143. [151]

    Russom, D

    P. Russom, D. Stodder, and F. Halper, ”Real-time data, BI, and analytics,” Accelerating Business to Leverage Customer Relations, Competitiveness, and Insights. TDWI best practices report, fourth quarter, pp. 5-25, 2014

    2014

  144. [152]

    Ramakrishnan and J

    R. Ramakrishnan and J. Gehrke, Database management systems . McGraw-Hill, Inc., 2002

    2002

  145. [153]

    Dell, ”What Does Generative AI Mean for Data Gravity and IT Infrastructure?,” 2024/10/06

    T. Dell, ”What Does Generative AI Mean for Data Gravity and IT Infrastructure?,” 2024/10/06

    2024

  146. [154]

    Solove, The Digital Person: Technology and Privacy in the Information Age

    D. Solove, The Digital Person: Technology and Privacy in the Information Age . New York Univer- sity Press, 2004

    2004

  147. [155]

    Available: https://www.dell.com/en-us/blog/what-does-generative-ai-mean-for-data-gravity-and-it-infrastructure/

    [Online]. Available: https://www.dell.com/en-us/blog/what-does-generative-ai-mean-for-data-gravity-and-it-infrastructure/

  148. [156]

    [Online]

    Dataiku, ”Why Data Quality Matters in the Age of Generative AI,” 2024/10/06 2024. [Online]. Available: https://blog.dataiku.com/why-data-quality-matters-in-the-age-of-generative-ai

    2024

  149. [157]

    Koltay, ”Data governance, data literacy and the management of data quality,” IFLA journal, vol

    T. Koltay, ”Data governance, data literacy and the management of data quality,” IFLA journal, vol. 42, no. 4, pp. 303-312, 2016

    2016

  150. [158]

    Juopperi, ”AI-driven SQL query optimization techniques,” 2024

    T. Juopperi, ”AI-driven SQL query optimization techniques,” 2024

    2024

  151. [159]

    Panwar, ”AI-Driven Query Optimization: Revolutionizing Database Performance and Effi- ciency.”

    V. Panwar, ”AI-Driven Query Optimization: Revolutionizing Database Performance and Effi- ciency.”

  152. [160]

    Bonchi, B

    F. Bonchi, B. Malin, and Y. Saygin, ”Recent advances in preserving privacy when mining data,” Data & Knowledge Engineering, vol. 65, no. 1, pp. 1-4, 2008

    2008

  153. [161]

    Arunachalam and R

    S. Arunachalam and R. De Wolf, ”Guest column: A survey of quantum learning theory,” ACM Sigact News, vol. 48, no. 2, pp. 41-67, 2017. 62

    2017

  154. [162]

    [Online]

    Ibm, ”IBM Watsonx AI Prompt Tuner,” 2024 2024. [Online]. Available: https://ibm.github. io/watsonx-ai-python-sdk/prompt_tuner.html

    2024

  155. [163]

    Holstein, J

    K. Holstein, J. Wortman Vaughan, H. Daum´ e III, M. Dudik, and H. Wallach, ”Improving fairness in machine learning systems: What do industry practitioners need?,” in Proceedings of the 2019 CHI conference on human factors in computing systems , 2019, pp. 1-16

    2019

  156. [164]

    Amazon Web, ”A WS Bedrock Prompt Flows,” 2024 2024

    S. Amazon Web, ”A WS Bedrock Prompt Flows,” 2024 2024. [Online]. Available: https://aws. amazon.com/bedrock/prompt-flows/

    2024

  157. [165]

    Google, ”Google Cloud Vertex AI Generative Text Prompts,” 2024 2024

    C. Google, ”Google Cloud Vertex AI Generative Text Prompts,” 2024 2024. [Online]. Available: https://cloud.google.com/vertex-ai/generative-ai/docs/text/text-prompts

    2024

  158. [166]

    Amazon Web

    S. Amazon Web. ”A WS SageMaker: End-to-End Machine Learning Platform for Building, Training, and Deploying Models.” https://aws.amazon.com/sagemaker/ (accessed

  159. [167]

    [Online]

    Microsoft, ”Microsoft Azure Prompt Flow,” 2024 2024. [Online]. Available: https://learn. microsoft.com/en-us/azure/ai-studio/how-to/prompt-flow

    2024

  160. [168]

    [Online]

    Microsoft, ”Microsoft Azure Machine Learning Registries and MLOps,” 2024 2024. [Online]. Avail- able: https://learn.microsoft.com/en-us/azure/machine-learning/concept-machine-learning-registries-mlops? view=azureml-api-2

    2024

  161. [169]

    [Online]

    Oracle, ”Oracle AI Data Science,” 2024/10/06. [Online]. Available: https://www.oracle.com/ artificial-intelligence/data-science/

    2024

  162. [170]

    Amazon Web, ”A WS SageMaker Model Registry,” 2024 2024

    S. Amazon Web, ”A WS SageMaker Model Registry,” 2024 2024. [Online]. Available: https: //docs.aws.amazon.com/sagemaker/latest/dg/model-registry-version.html

    2024

  163. [171]

    Google, ”Google Cloud Vertex AI Model Registry Introduction,” 2024 2024

    C. Google, ”Google Cloud Vertex AI Model Registry Introduction,” 2024 2024. [Online]. Available: https://cloud.google.com/vertex-ai/docs/model-registry/introduction

    2024

  164. [172]

    [Online]

    Microsoft, ”Microsoft Azure MLOps Setup,” 2024 2024. [Online]. Available: https://learn. microsoft.com/en-us/azure/machine-learning/how-to-setup-mlops-azureml?view=azureml-api-2& tabs=azure-shell. 63

    2024

  165. [173]

    [Online]

    Oracle, ”Oracle Data Science Model Catalog,” 2024 2024. [Online]. Available: https://docs. oracle.com/en-us/iaas/data-science/using/models-about.htm

    2024

  166. [174]

    Amazon Web, ”A WS SageMaker Lineage Tracking,” 2024 2024

    S. Amazon Web, ”A WS SageMaker Lineage Tracking,” 2024 2024. [Online]. Available: https: //docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html

    2024

  167. [175]

    Google, ”Google Cloud Vertex AI ML Metadata,” 2024 2024

    C. Google, ”Google Cloud Vertex AI ML Metadata,” 2024 2024. [Online]. Available: https: //cloud.google.com/vertex-ai/docs/ml-metadata/introduction

    2024

  168. [176]

    [Online]

    Oracle, ”Oracle Data Science Models Overview,” 2024 2024. [Online]. Available: https://docs. oracle.com/en-us/iaas/data-science/using/models-about.htm

    2024

  169. [177]

    ”IBM Watson Studio: Collaborative Environment for AI Model Building and Deployment.” https://www.ibm.com/products/watson-studio (accessed

    Ibm. ”IBM Watson Studio: Collaborative Environment for AI Model Building and Deployment.” https://www.ibm.com/products/watson-studio (accessed

  170. [178]

    Amazon Web, ”A WS SageMaker Projects Overview,” 2024 2024

    S. Amazon Web, ”A WS SageMaker Projects Overview,” 2024 2024. [Online]. Available: https: //docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-projects-whatis.html

    2024

  171. [179]

    Google, ”Google Cloud Vertex AI Pipelines Introduction,” 2024 2024

    C. Google, ”Google Cloud Vertex AI Pipelines Introduction,” 2024 2024. [Online]. Available: https://cloud.google.com/vertex-ai/docs/pipelines/introduction

    2024

  172. [180]

    Amazon Web, ”Amazon SageMaker Serverless Inference: Machine Learning Inference With- out Worrying About Servers,” 2024 2024

    S. Amazon Web, ”Amazon SageMaker Serverless Inference: Machine Learning Inference With- out Worrying About Servers,” 2024 2024. [Online]. Available: https://aws.amazon.com/blogs/aws/ amazon-sagemaker-serverless-inference-machine-learning-inference-without-worrying-about-servers/

    2024

  173. [181]

    [Online]

    Oracle, ”Oracle DevOps Service,” 2024/10/06. [Online]. Available: https://www.oracle.com/ devops/devops-service/

    2024

  174. [182]

    [Online]

    Microsoft, ”Microsoft Azure Container Instances,” 2024 2024. [Online]. Available: https:// azure.microsoft.com/en-us/products/container-instances

    2024

  175. [183]

    Amazon Web, ”Amazon SageMaker Neo,” 2024 2024

    S. Amazon Web, ”Amazon SageMaker Neo,” 2024 2024. [Online]. Available: https://aws. amazon.com/sagemaker/neo/

    2024

  176. [184]

    [Online]

    Microsoft, ”Microsoft Support: All About Neural Processing Units (NPUs),” 2024 2024. [Online]. Available: https://support.microsoft.com/en-us/windows/all-about-neural-processing-units-npus-e77a5637-7705-4915-96c8-0c6a975f9db4

    2024

  177. [185]

    [Online]

    Microsoft, ”Microsoft Azure ONNX Concept Overview,” 2024 2024. [Online]. Available: https: //learn.microsoft.com/en-us/azure/machine-learning/concept-onnx?view=azureml-api-2

    2024

  178. [186]

    A. I. Google, ”Google AI Edge LiteRT,” 2024 2024. [Online]. Available: https://ai.google. dev/edge/litert. 64

    2024

  179. [187]

    C. Google. ”Google Cloud Run.” https://cloud.google.com/run?hl=en (accessed

  180. [188]

    I. B. M. Cloud. ”IBM Cloud for AI.” https://www.ibm.com/cloud/ai (accessed

  181. [189]

    C. Google. ”Accelerate AI development with Google Cloud TPUs.” https://cloud.google.com/ tpu?hl=en (accessed

  182. [190]

    [Online]

    Oracle, ”Oracle Cloud GPU Compute,” 2024/10/06. [Online]. Available: https://www.oracle. com/cloud/compute/gpu/

    2024

  183. [191]

    ”Oracle Functions Overview.”https://docs.oracle.com/en-us/iaas/Content/Functions/ Concepts/functionsoverview.htm (accessed

    Oracle. ”Oracle Functions Overview.”https://docs.oracle.com/en-us/iaas/Content/Functions/ Concepts/functionsoverview.htm (accessed

  184. [192]

    Google, ”Google Cloud Monitoring,” 2024 2024

    C. Google, ”Google Cloud Monitoring,” 2024 2024. [Online]. Available: https://cloud.google. com/monitoring?hl=en

    2024

  185. [193]

    Microsoft

    C. Microsoft. ”Concept of Model Monitoring in Azure Machine Learning.” https://learn. microsoft.com/en-us/azure/machine-learning/concept-model-monitoring?view=azureml-api-2 (accessed

  186. [194]

    [Online]

    Ibm, ”Watson OpenScale | IBM Cloud Paks,” 2024/10/06 2024. [Online]. Available: https: //www.ibm.com/docs/en/cloud-paks/cp-data/5.0.x?topic=new-watson-openscale

    2024

  187. [195]

    Amazon Web, ”Amazon SageMaker Model Monitor: Fully Managed Automatic Monitoring for Your Machine Learning Models,” 2024/10/06 2024

    S. Amazon Web, ”Amazon SageMaker Model Monitor: Fully Managed Automatic Monitoring for Your Machine Learning Models,” 2024/10/06 2024. [Online]. Available: https://aws.amazon.com/ blogs/aws/amazon-sagemaker-model-monitor-fully-managed-automatic-monitoring-for-your-machine-lear...

    2024

  188. [196]

    Amazon Web

    S. Amazon Web. ”Detecting Data Drift Using Amazon SageMaker.” Amazon Web Services. https: //aws.amazon.com/blogs/architecture/detecting-data-drift-using-amazon-sagemaker/ (ac- cessed

  189. [197]

    Google, ”Model Monitoring Overview | Google Cloud Vertex AI,” 2024/10/06 2024

    C. Google, ”Model Monitoring Overview | Google Cloud Vertex AI,” 2024/10/06 2024. [Online]. Available: https://cloud.google.com/vertex-ai/docs/model-monitoring/overview

    2024

  190. [198]

    Microsoft

    A. Microsoft. ”How to Use the Responsible AI Dashboard in Azure Machine Learning.” https:// learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard?view= azureml-api-2 (accessed

  191. [199]

    I. B. M. Corporation. ”Watson Drift Metrics in IBM Watson OpenScale.” https://www.ibm.com/ docs/en/watsonx/w-and-w/1.1.x?topic=openscale-watson-drift-metrics (accessed

  192. [200]

    Amazon Web, ”SageMaker Clarify,” 2024/10/06

    S. Amazon Web, ”SageMaker Clarify,” 2024/10/06. [Online]. Available: https://aws.amazon. com/sagemaker/clarify/

    2024

  193. [201]

    C. Google. ”Explainable AI in Google Cloud Vertex AI: Overview of Interpretability Tools.” https://cloud.google.com/vertex-ai/docs/explainable-ai/overview (accessed. 65

  194. [202]

    ”Let's Be Fair: Using AI Fairness Tools to Ensure Ethical AI.”https://www.ateam-oracle

    Oracle. ”Let's Be Fair: Using AI Fairness Tools to Ensure Ethical AI.”https://www.ateam-oracle. com/post/lets-be-fair-using-ai (accessed

  195. [203]

    ”AI Explainability 360: IBM 's Open-Source Toolkit for Explainable AI.” https://aix360

    Ibm. ”AI Explainability 360: IBM 's Open-Source Toolkit for Explainable AI.” https://aix360. res.ibm.com/ (accessed

  196. [204]

    Amazon Web

    S. Amazon Web. ”A WS Bedrock: Build and Scale Generative AI Applications with Pre-Trained Models.” https://aws.amazon.com/bedrock/ (accessed

  197. [205]

    C. Google. ”Vertex AI.” https://cloud.google.com/vertex-ai (accessed

  198. [206]

    [Online]

    Oracle, ”Oracle AI Language Service,” 2024/10/06. [Online]. Available: https://www.oracle. com/artificial-intelligence/language/

    2024

  199. [207]

    [Online]

    Ibm, ”IBM Natural Language Understanding,” 2024/10/06 2024. [Online]. Available: https: //www.ibm.com/products/natural-language-understanding

    2024

  200. [208]

    [Online]

    Ibm, ”IBM Video Streaming,” 2024/10/06. [Online]. Available: https://www.ibm.com/products/ video-streaming

    2024

  201. [209]

    [Online]

    Google, ”Google Imagen Research,” 2024/10/06. [Online]. Available: https://imagen.research. google/

    2024

  202. [210]

    [Online]

    GitHub, ”GitHub Copilot,” 2024/10/06. [Online]. Available: https://github.com/features/ copilot

    2024

  203. [211]

    [Online]

    Oracle, ”Oracle AI Vision,” 2024/10/06. [Online]. Available: https://www.oracle.com/ artificial-intelligence/vision/

    2024

  204. [212]

    [Online]

    Aws, ”A WS CodeWhisperer,” 2024/10/06. [Online]. Available: https://docs.aws.amazon.com/ codewhisperer/latest/userguide/what-is-cwspr.html

    2024

  205. [213]

    [Online]

    Google, ”Google Cloud AI Code Generation,” 2024/10/06. [Online]. Available: https://cloud. google.com/use-cases/ai-code-generation?hl=en

    2024

  206. [214]

    66 [Online]

    Microsoft, ”Azure AI Speech Launches New Zero-Shot TTS Models for Personal Use,” 2024/10/06. 66 [Online]. Available: https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/ azure-ai-speech-launches-new-zero-shot-tts-models-for-personal/ba-p/4044521

    2024

  207. [215]

    [Online]

    Ibm, ”IBM Watsonx Code Assistant,” 2024/10/06. [Online]. Available: https://www.ibm.com/ products/watsonx-code-assistant

    2024

  208. [216]

    Amazon Web

    S. Amazon Web. ”A WS Polly: Text-to-Speech Service for Generating Realistic Speech.” https: //aws.amazon.com/polly/ (accessed

  209. [217]

    C. Google. ”Google Cloud Speech-to-Text: Speech Recognition and Transcription Services.”https: //cloud.google.com/speech-to-text?hl=en (accessed

  210. [218]

    [Online]

    Oracle, ”Oracle AI Speech,” 2024/10/06. [Online]. Available: https://www.oracle.com/ artificial-intelligence/speech/

    2024

  211. [219]

    [Online]

    Ibm, ”IBM Text-to-Speech,” 2024/10/06. [Online]. Available: https://www.ibm.com/products/ text-to-speech

    2024

  212. [220]

    Microsoft

    A. Microsoft. ”Microsoft Azure AI Bot Service.” https://azure.microsoft.com/en-us/ products/ai-services/ai-bot-service (accessed

  213. [221]

    Google, ”Get Multimodal Embeddings with Vertex AI,” 2024/10/06 2024

    C. Google, ”Get Multimodal Embeddings with Vertex AI,” 2024/10/06 2024. [Online]. Available: https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings

    2024

  214. [222]

    Amazon Web

    S. Amazon Web. ”Amazon Lex V2 Documentation.” https://docs.aws.amazon.com/lexv2/ latest/dg/what-is.html (accessed

  215. [223]

    C. Google. ”Cloud Google Dialogflow CX Documentation.” https://cloud.google.com/ dialogflow/cx/docs (accessed

  216. [224]

    ”Oracle Digital Assistant Documentation.” https://www.oracle.com/chatbots/what-is-a-digital-assistant/ (accessed

    Oracle. ”Oracle Digital Assistant Documentation.” https://www.oracle.com/chatbots/what-is-a-digital-assistant/ (accessed

  217. [225]

    ”IBM WatsonX Assistant Documentation.”https://www.ibm.com/products/watsonx-assistant (accessed

    Ibm. ”IBM WatsonX Assistant Documentation.”https://www.ibm.com/products/watsonx-assistant (accessed

  218. [226]

    C. Google. ”Google Cloud Translate Service Overview.” https://cloud.google.com/translate? hl=en (accessed

  219. [227]

    Microsoft

    A. Microsoft. ”Microsoft Azure Translator Service Overview.” https://learn.microsoft.com/ en-us/azure/ai-services/translator/translator-overview (accessed

  220. [228]

    ”Oracle AI Language Service Overview.”https://www.oracle.com/artificial-intelligence/ language/ (accessed

    Oracle. ”Oracle AI Language Service Overview.”https://www.oracle.com/artificial-intelligence/ language/ (accessed

  221. [229]

    Amazon Web

    S. Amazon Web. ”Amazon Translate Service Overview.” https://docs.aws.amazon.com/ translate/latest/dg/what-is.html (accessed. 67

  222. [230]

    Newman, Building microservices

    S. Newman, Building microservices. ” O 'Reilly Media, Inc.”, 2021

    2021

  223. [231]

    C. Google. ”Google Cloud Natural Language API: Text Analysis and Language Understanding Platform.” https://cloud.google.com/natural-language?hl=en (accessed

  224. [232]

    Janapa Reddi, Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems

    V. Janapa Reddi, Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems . Harvard University, 2024

    2024

  225. [233]

    Felstaine and O

    E. Felstaine and O. Hermoni, ”Machine Learning, Containers, Cloud Natives, and Microservices,” in Artificial Intelligence for Autonomous Networks : Chapman and Hall/CRC, 2018, pp. 145-164

    2018

  226. [234]

    Microsoft, ”Jailbreak Detection | Microsoft Azure AI Content Safety,” 2024/10/06 2024

    A. Microsoft, ”Jailbreak Detection | Microsoft Azure AI Content Safety,” 2024/10/06 2024. [Online]. Available: https://learn.microsoft.com/en-us/azure/ai-services/content-safety/ concepts/jailbreak-detection

    2024

  227. [235]

    M. S. Aslanpour, S. S. Gill, and A. N. Toosi, ”Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research,”Internet of Things, vol. 12, p. 100273, 2020/12/01/ 2020, doi: https://doi.org/10.1016/j.iot.2020.100273

    2020

  228. [236]

    Microsoft, ”Confidential Compute Solutions | Microsoft Azure,” 2024/10/06 2024

    A. Microsoft, ”Confidential Compute Solutions | Microsoft Azure,” 2024/10/06 2024. [Online]. Available: https://azure.microsoft.com/en-us/solutions/confidential-compute

    2024

  229. [237]

    ”Oracle Confidential Computing: Secure Data Processing in the Cloud.” https://docs

    Oracle. ”Oracle Confidential Computing: Secure Data Processing in the Cloud.” https://docs. oracle.com/en-us/iaas/Content/Compute/References/confidential_compute.htm (accessed

  230. [238]

    Google, ”Confidential Computing Products | Google Cloud,” 2024/10/06 2024

    C. Google, ”Confidential Computing Products | Google Cloud,” 2024/10/06 2024. [Online]. Avail- able: https://cloud.google.com/security/products/confidential-computing?hl=en

    2024

  231. [239]

    Amazon Web, ”Nitro Enclaves | Amazon EC2,” 2024/10/06 2024

    S. Amazon Web, ”Nitro Enclaves | Amazon EC2,” 2024/10/06 2024. [Online]. Available: https: //aws.amazon.com/ec2/nitro/nitro-enclaves/

    2024

  232. [240]

    Amazon Web

    S. Amazon Web. ”Generative AI Application Builder on A WS: Create AI-Powered Applications with Pre-Built Solutions.” https://aws.amazon.com/solutions/implementations/generative-ai-application-builder-on-aws/ (accessed

  233. [241]

    I. B. M. Research, ”Fully Homomorphic Encryption,” 2024/10/06. [Online]. Available: https: //research.ibm.com/topics/fully-homomorphic-encryption

    2024

  234. [242]

    C. Google. ”Google Cloud Anthos: Hybrid and Multi-Cloud Application Management Platform.” https://cloud.google.com/anthos?hl=en (accessed

  235. [243]

    Microsoft

    A. Microsoft. ”Azure Automated Machine Learning: AI-Powered Tools for Automated Model Build- 68 ing and Deployment.”https://azure.microsoft.com/en-us/solutions/automated-machine-learning (accessed

  236. [244]

    Microsoft

    A. Microsoft. ”Azure Functions on Kubernetes (v1) Reference.” https://learn.microsoft.com/ en-us/azure/devops/pipelines/tasks/reference/azure-function-on-kubernetes-v1?view=azure-pipelines (accessed

  237. [245]

    ”Kubernetes Documentation: Home Page and Resource Overview.” https:// kubernetes.io/docs/home/ (accessed

    Kubernetes. ”Kubernetes Documentation: Home Page and Resource Overview.” https:// kubernetes.io/docs/home/ (accessed

  238. [246]

    ”AI Fairness 360: IBM 's Open-Source Toolkit for Ensuring Fairness in AI.”https://aif360

    Ibm. ”AI Fairness 360: IBM 's Open-Source Toolkit for Ensuring Fairness in AI.”https://aif360. res.ibm.com/ (accessed

  239. [247]

    [Online]

    Microsoft, ”Fairness in Machine Learning Concepts,” 2024/10/06. [Online]. Available: https:// learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml?view=azureml-api-2

    2024

  240. [248]

    Microsoft

    A. Microsoft. ”Azure Machine Learning Interpretability: Tools for Model Transparency and In- sights.” https://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability? view=azureml-api-2 (accessed

  241. [249]

    ”Unlocking Fairness: A Trade-off Revisited in AI Development.” https://blogs.oracle

    Oracle. ”Unlocking Fairness: A Trade-off Revisited in AI Development.” https://blogs.oracle. com/ai-and-datascience/post/unlocking-fairness-a-trade-off-revisited (accessed

  242. [250]

    Amazon Web

    S. Amazon Web. ”Amazon SageMaker Debugger: Monitor and Debug Machine Learning Models.” https://aws.amazon.com/sagemaker/debugger/ (accessed

  243. [251]

    Microsoft

    A. Microsoft. ”How to Use Machine Learning Interpretability in Azure Machine Learning.” https: //learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability? view=azureml-api-2 (accessed

  244. [252]

    Amazon Web

    S. Amazon Web. ”A WS Responsible AI: Principles and Practices for Ethical AI Development.” https://aws.amazon.com/ai/responsible-ai/ (accessed

  245. [253]

    Amazon Web

    S. Amazon Web. ”A WS Responsible AI Resources: Tools and Guidelines for Ethical AI Develop- ment.” https://aws.amazon.com/ai/responsible-ai/resources/ (accessed

  246. [254]

    ”Manage AI Models with IBM Watson OpenScale.” https://mediacenter.ibm.com/media/ Manage+AI+models+with+IBM+Watson+OpenScale/1_lqiuc2uq (accessed

    Ibm. ”Manage AI Models with IBM Watson OpenScale.” https://mediacenter.ibm.com/media/ Manage+AI+models+with+IBM+Watson+OpenScale/1_lqiuc2uq (accessed

  247. [255]

    A. I. Google. ”Responsible Generative AI Toolkit.” https://ai.google.dev/responsible (ac- cessed. 69

  248. [256]

    T. A. Assegie, ”Evaluation of local interpretable model-agnostic explanation and shapley additive explanation for chronic heart disease detection,” Proc Eng Technol Innov, vol. 23, pp. 48-59, 2023

    2023

  249. [257]

    ”Responsible AI for Healthcare and Financial Services: Addressing Ethical AI Chal- lenges.” https://blogs.oracle.com/ai-and-datascience/post/responsible-ai-for-hc-and-fsi (accessed

    Oracle. ”Responsible AI for Healthcare and Financial Services: Addressing Ethical AI Chal- lenges.” https://blogs.oracle.com/ai-and-datascience/post/responsible-ai-for-hc-and-fsi (accessed

  250. [258]

    C. Google. ”Google Cloud Preemptible Instances: Cost-Effective Compute for Short-Term Work- loads.” https://cloud.google.com/compute/docs/instances/preemptible (accessed

  251. [259]

    Amazon Web

    S. Amazon Web. ”A WS Pricing: Explore Pricing Models and Cost Management for A WS Services.” https://aws.amazon.com/pricing/ (accessed

  252. [261]

    Microsoft

    A. Microsoft. ”Azure Spot Pricing: Optimize Cloud Costs with Unused Compute Capacity.” https://azure.microsoft.com/en-us/pricing/spot/ (accessed. 70 Supplementary Material S1 Extended Technical Details S1.1 Evolution of Generative AI This section provides an expanded timeline ...

    2014

  253. [262]

    High-Performance Computing (HPC)

  254. [263]

    Serverless Architectures

  255. [264]

    Data Lakes and Warehousing

  256. [265]

    Data Versioning and Management

  257. [266]

    High-Speed Networking

  258. [267]

    Content Delivery Networks (CDNs)

  259. [268]

    Pre-trained Models and APIs

  260. [269]

    Data Labeling and Annotation Tools

  261. [270]

    Data Privacy and Compliance

  262. [271]

    For instance, HPC services are essential for training large models, while edge computing solutions enable real-time AI applications

    AI Model Security Each category is crucial for different aspects of generative AI development and deployment. For instance, HPC services are essential for training large models, while edge computing solutions enable real-time AI applications. The table allows for a side-by-sid...

    2020

  263. [272]

    The chronological distribution shows a significant increase in relevant publications over the past five years: • 2015: 4 references • 2016: 1 reference • 2017: 7 references • 2018: 4 references • 2019: 4 references • 2020: 6 references • 2021: 8 references • 2022: 4 references...

    2015

  264. [273]

    Cloud Providers: 60 references (23.5%)

  265. [274]

    Cloud Computing: 52 references (20.4%)

  266. [275]

    Generative AI: 45 references (17.6%) 86

  267. [276]

    Artificial Intelligence: 38 references (14.9%)

  268. [277]

    AI Development: 25 references (9.8%)

  269. [278]

    Security & Compliance: 10 references (3.9%)

  270. [279]

    Performance & Scalability: 8 references (3.1%)

  271. [280]

    Edge Computing & IoT: 7 references (2.7%)

  272. [281]

    Quantum Computing: 5 references (2.0%)

  273. [282]

    Key findings include: Cloud Providers: • A WS: 45 references 87 Figure S12: Distribution of references across key dimensions of cloud-based generative AI

    Ethical AI & Governance: 5 references (2.0%) The keyword analysis (Figure S11, right) corroborates this, with the following being the most frequent terms: • Cloud: 185 occurrences • AI/Artificial Intelligence: 172 occurrences • Machine Learning: 135 occurrences • Generative AI...

    2020

  274. [283]

    Cloud Providers (60 refs, 23.5%)

  275. [284]

    Cloud Computing (52 refs, 20.4%)

  276. [285]

    Generative AI (45 refs, 17.6%) Top Keywords

  277. [286]

    Cloud (185 occurrences)

  278. [287]

    AI/Artificial Intelligence (172 occurrences)

  279. [288]

    The focus is decidedly on recent developments, with a strong emphasis on practical, industry-oriented sources balanced by academic research

    Machine Learning (135 occurrences) Matrix Highlights • A WS leads in references (45) • Text generation most covered (60 refs) • Infrastructure focus (55 refs) 89 This analysis provides a clear picture of the current state of research on cloud platforms for developing generativ...

  280. [2024]

    Available: https://www.missioncloud.com/blog/rag-generative-ai

    [Online]. Available: https://www.missioncloud.com/blog/rag-generative-ai

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.