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| 001 | 979-8-8688-1076-3 | ||
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_a9798868810763 _9979-8-8688-1076-3 |
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_a10.1007/979-8-8688-1076-3 _2doi |
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| 050 | 4 | _aTA347.A78 | |
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| 100 | 1 |
_aSendas, Neel. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _990164 |
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| 245 | 1 | 4 |
_aThe Definitive Guide to Machine Learning Operations in AWS _h[electronic resource] : _bMachine Learning Scalability and Optimization with AWS / _cby Neel Sendas, Deepali Rajale. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aBerkeley, CA : _bApress : _bImprint: Apress, _c2024. |
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| 300 |
_aXVIII, 440 p. 128 illus., 127 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _aChapter 1: Introduction to MLOps -- Chapter 2: Foundations of MLOps on AWS -- Chapter 3: Operational Excellence in MLOps -- Chapter 4: Security in MLOps -- Chapter 5: Reliability in MLOps -- Chapter 6: Performance Efficiency in MLOps -- Chapter 7: Cost Optimization in MLOps -- Chapter 8 MLOps Best Practices and Case Studies -- Chapter 9: MLOps for GenAI -- Chapter 10: Future Trends in MLOps. | |
| 520 | _aThis book focuses on deploying, testing, monitoring, and automating ML systems in production at Cloud scale. It covers AWS MLOps services such as Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS. This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected Framework. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. The book concludes with MLOps best practices, MLOps for GenAI, emerging trends, and future developments in MLOps By the end, readers will learn operating ML workloads on the AWS cloud. This book suits software developers, ML engineers, DevOps engineers, architects, and team leaders aspiring to be MLOps professionals on AWS. What you will learn: ● Create repeatable training workflows to accelerate model development ● Catalog ML artifacts centrally for model reproducibility and governance ● Integrate ML workflows with CI/CD pipelines for faster time to production ● Continuously monitor data and models in production to maintain quality ● Optimize model deployment for performance and cost. | ||
| 650 | 0 |
_aArtificial intelligence. _958441 |
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| 650 | 0 |
_aCloud Computing. _958386 |
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| 650 | 0 |
_aProduction management. _967141 |
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| 650 | 1 | 4 |
_aArtificial Intelligence. _958441 |
| 650 | 2 | 4 |
_aCloud Computing. _958386 |
| 650 | 2 | 4 |
_aOperations Management. _967304 |
| 700 | 1 |
_aRajale, Deepali. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _990165 |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9798868810756 |
| 776 | 0 | 8 |
_iPrinted edition: _z9798868810770 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/979-8-8688-1076-3 |
| 912 | _aZDB-2-CWD | ||
| 912 | _aZDB-2-SXPC | ||
| 999 |
_c66342 _d66342 |
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