Machine learning engineering with Python : (Record no. 199986)
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| fixed length control field | 03639nam a22002537a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260520104731.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260520b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781801079259 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 MCM-M |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | McMahon, Andrew P. |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning engineering with Python : |
| Remainder of title | manage the production life cycle of machine learning models using MLOps with practical examples |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Birmingham - Mumbai |
| Name of publisher | Packt Publishing |
| Year of publication | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xiv, 260p. |
| 500 ## - GENERAL NOTE | |
| General note | Author: Andrew Peter (Andy) McMahon, PhD, ML Engineering Lead / Head of MLOps, NatWest Group, UK; previously Analytics Team Lead, Aggreko. PhD in Condensed Matter Physics, Imperial College London; BSc Theoretical Physics, University of Glasgow. Named Data Scientist of the Year 2019 (Data Science Foundation) and Rising Star of the Year 2022 (British Data Awards). Co-host of the AI Right podcast. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Preface -- Section 1. Setting the Scene -- Ch. 1. Introduction to ML engineering -- 1.1. Data roles in modern organisations -- 1.2. What is ML engineering? -- 1.3. Isolating appropriate ML problems -- 1.4. High-level ML system design -- Ch. 2. The machine learning development process -- 2.1. The ML development lifecycle -- 2.2. Agile for ML projects -- 2.3. Tools for tracking and planning -- Section 2. ML Development and Deployment -- Ch. 3. From model to model factory -- 3.1. Building training systems -- 3.2. Hyperparameter optimisation -- 3.3. Model management with MLflow -- Ch. 4. Packaging up -- 4.1. Object-oriented ML code -- 4.2. Functional programming for ML -- 4.3. Building ML libraries and packages -- Ch. 5. Deployment patterns and tools -- 5.1. ML microservices -- 5.2. Containerising with Docker -- 5.3. Orchestration with Kubernetes -- Ch. 6. Scaling up -- 6.1. Distributed computing with PySpark -- 6.2. Cloud-based scaling (AWS) -- 6.3. Monitoring and performance tracking -- Section 3. End-to-End Examples -- Ch. 7. Building an example ML microservice -- Ch. 8. Building an Extract Transform Machine Learning (ETML) use case -- Index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | A practical guide to machine learning engineering for ML engineers, data scientists, and software developers seeking to productionise machine learning solutions using Python and modern MLOps practices. The book covers the complete ML production lifecycle across three sections. Section 1 introduces ML engineering roles, the ML development lifecycle, agile methodologies for ML projects, and high-level ML system design. Section 2 covers the core technical skills: building training systems and model factories, hyperparameter optimisation, model management with MLflow, object-oriented and functional programming for ML, building ML libraries and packages, deployment patterns including ML microservices, containerisation with Docker, orchestration with Kubernetes, scaling with PySpark and AWS, and monitoring and performance tracking. Section 3 provides two end-to-end worked examples: building a deployable ML microservice and an Extract, Transform, and Load (ETL) use case. Tools covered include scikit-learn, MLflow, Docker, Kubernetes, PySpark, FastAPI, and AWS. Code files available on GitHub. The first edition (2021) and the second edition (2023) add generative AI and LLM coverage. Suitable for intermediate-to-advanced Python users working in ML engineering, data science, or software development. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial intelligence |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Python |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books and Monographs |
| Full call number | Accession Number | Koha item type | Lost status | Damaged status | Permanent Location | Current Location | Shelving location | Date acquired | Source of acquisition |
|---|---|---|---|---|---|---|---|---|---|
| 006.31 MCM-M | 102778 | Books and Monographs | Central Library, NIT Jalandhar | Central Library, NIT Jalandhar | General Stacks | 20.05.2026 | Mumbai, TV Enterprises | ||
| 006.31 MCM-M | 102779 | Books and Monographs | Central Library, NIT Jalandhar | Central Library, NIT Jalandhar | General Stacks | 20.05.2026 | Mumbai, TV Enterprises |