Machine Learning Engineering with Python - Manage the Lifecycle of Machine Learning Models Using MLOps with Practical Examples (2nd Edition)
This is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concep...
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| Main Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Birmingham
Packt Publishing
2023
Packt Publishing, Limited Packt Publishing Limited |
| Edition: | 2 |
| Subjects: | |
| ISBN: | 9781837631964, 1837631964 |
| Online Access: | Get full text |
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Table of Contents:
- Title Page Preface Table of Contents 1. Introduction to ML Engineering 2. The Machine Learning Development Process 3. From Model to Model Factory 4. Packaging Up 5. Deployment Patterns and Tools 6. Scaling Up 7. Deep Learning, Generative AI, and LLMOps 8. Building an Example ML Microservice 9. Building an Extract, Transform, Machine Learning Use Case Index
- Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introduction to ML Engineering -- Technical requirements -- Defining a taxonomy of data disciplines -- Data scientist -- ML engineer -- ML operations engineer -- Data engineer -- Working as an effective team -- ML engineering in the real world -- What does an ML solution look like? -- Why Python? -- High-level ML system design -- Example 1: Batch anomaly detection service -- Example 2: Forecasting API -- Example 3: Classification pipeline -- Summary -- Chapter 2: The Machine Learning Development Process -- Technical requirements -- Setting up our tools -- Setting up an AWS account -- Concept to solution in four steps -- Comparing this to CRISP-DM -- Discover -- Using user stories -- Play -- Develop -- Selecting a software development methodology -- Package management (conda and pip) -- Poetry -- Code version control -- Git strategies -- Model version control -- Deploy -- Knowing your deployment options -- Understanding DevOps and MLOps -- Building our first CI/CD example with GitHub Actions -- Continuous model performance testing -- Continuous model training -- Summary -- Chapter 3: From Model to Model Factory -- Technical requirements -- Defining the model factory -- Learning about learning -- Defining the target -- Cutting your losses -- Preparing the data -- Engineering features for machine learning -- Engineering categorical features -- Engineering numerical features -- Designing your training system -- Training system design options -- Train-run -- Train-persist -- Retraining required -- Detecting data drift -- Detecting concept drift -- Setting the limits -- Diagnosing the drift -- Remediating the drift -- Other tools for monitoring -- Automating training -- Hierarchies of automation -- Optimizing hyperparameters -- Hyperopt -- Optuna -- AutoML -- auto-sklearn
- Going deep with deep learning -- Getting started with PyTorch -- Scaling and taking deep learning into production -- Fine-tuning and transfer learning -- Living it large with LLMs -- Understanding LLMs -- Consuming LLMs via API -- Coding with LLMs -- Building the future with LLMOps -- Validating LLMs -- PromptOps -- Summary -- Chapter 8: Building an Example ML Microservice -- Technical requirements -- Understanding the forecasting problem -- Designing our forecasting service -- Selecting the tools -- Training at scale -- Serving the models with FastAPI -- Response and request schemas -- Managing models in your microservice -- Pulling it all together -- Containerizing and deploying to Kubernetes -- Containerizing the application -- Scaling up with Kubernetes -- Deployment strategies -- Summary -- Chapter 9: Building an Extract, Transform, Machine Learning Use Case -- Technical requirements -- Understanding the batch processing problem -- Designing an ETML solution -- Selecting the tools -- Interfaces and storage -- Scaling of models -- Scheduling of ETML pipelines -- Executing the build -- Building an ETML pipeline with advanced Airflow features -- Summary -- Packt Page -- Other Books You May Enjoy -- Index
- AutoKeras -- Persisting your models -- Building the model factory with pipelines -- Scikit-learn pipelines -- Spark ML pipelines -- Summary -- Chapter 4: Packaging Up -- Technical requirements -- Writing good Python -- Recapping the basics -- Tips and tricks -- Adhering to standards -- Writing good PySpark -- Choosing a style -- Object-oriented programming -- Functional programming -- Packaging your code -- Why package? -- Selecting use cases for packaging -- Designing your package -- Building your package -- Managing your environment with Makefiles -- Getting all poetic with Poetry -- Testing, logging, securing, and error handling -- Testing -- Securing your solutions -- Analyzing your own code for security issues -- Analyzing dependencies for security issues -- Logging -- Error handling -- Not reinventing the wheel -- Summary -- Chapter 5: Deployment Patterns and Tools -- Technical requirements -- Architecting systems -- Building with principles -- Exploring some standard ML patterns -- Swimming in data lakes -- Microservices -- Event-based designs -- Batching -- Containerizing -- Hosting your own microservice on AWS -- Pushing to ECR -- Hosting on ECS -- Building general pipelines with Airflow -- Airflow -- Airflow on AWS -- Revisiting CI/CD for Airflow -- Building advanced ML pipelines -- Finding your ZenML -- Going with the Kubeflow -- Selecting your deployment strategy -- Summary -- Chapter 6: Scaling Up -- Technical requirements -- Scaling with Spark -- Spark tips and tricks -- Spark on the cloud -- AWS EMR example -- Spinning up serverless infrastructure -- Containerizing at scale with Kubernetes -- Scaling with Ray -- Getting started with Ray for ML -- Scaling your compute for Ray -- Scaling your serving layer with Ray -- Designing systems at scale -- Summary -- Chapter 7: Deep Learning, Generative AI, and LLMOps
- Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples