Recruitment of participants Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOp

Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples, Second Edition



Key benefits

• 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

Description

The Second Edition of Machine Learning Engineering with Python 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 concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.

What you will learn

• 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