Machine learning engineering with Python : manage the production life cycle of machine learning models using MLOps with practical examples
Material type:
TextLanguage: English Publication details: Birmingham - Mumbai Packt Publishing 2021Description: xiv, 260pISBN: - 9781801079259
- 006.31 MCM-M
| Item type | Current library | Home library | Collection | Call number | Materials specified | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|---|---|
| Books and Monographs | Central Library, NIT Jalandhar General Stacks | Central Library, NIT Jalandhar | Center for Artificial Intelligence | 006.31 MCM-M (Browse shelf(Opens below)) | Available | 102778 | |||
| Books and Monographs | Central Library, NIT Jalandhar General Stacks | Central Library, NIT Jalandhar | Center for Artificial Intelligence | 006.31 MCM-M (Browse shelf(Opens below)) | Available | 102779 |
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.
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.
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.