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Machine Learning Engineering with Python
Manage the lifecycle of machine learning models using MLOps with practical examples
Andrew P. McMahon
Computers / Software Development & Engineering / Quality Assurance & Testing
Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain
Key Features- This second edition delves deeper into key machine learning topics, CI/CD, and system design
- Explore core MLOps practices, such as model management and performance monitoring
- Build end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools
- Plan and manage end-to-end ML development projects
- Explore deep learning, LLMs, and LLMOps to leverage generative AI
- Use Python to package your ML tools and scale up your solutions
- Get to grips with Apache Spark, Kubernetes, and Ray
- Build and run ML pipelines with Apache Airflow, ZenML, and Kubeflow
- Detect drift and build retraining mechanisms into your solutions
- Improve error handling with control flows and vulnerability scanning
- Host and build ML microservices and batch processes running on AWS
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