000			03639nam a22002537a 4500
003			OSt
005			20260520104731.0
008			260520b |||||||| |||| 00| 0 eng d
020			_a9781801079259
041			_aeng
082			_a006.31 MCM-M
100			_aMcMahon, Andrew P. _979220
245			_aMachine learning engineering with Python : _bmanage the production life cycle of machine learning models using MLOps with practical examples
260			_aBirmingham - Mumbai _bPackt Publishing _c2021
300			_axiv, 260p.
500			_aAuthor: 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			_aPreface -- 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			_aA 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			_aArtificial intelligence _979221
650			_aMachine learning. _979222
650			_aPython _979223
942			_cBK
999			_c199986 _d199986