Machine learning engineering with python - second edition: manage the lifecycle of...

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Описание

This second edition delves deeper into key machine learning topics, CI/CD, and
This second edition delves deeper into key machine learning topics, CI/CD, and
problems

Includes a new chapter on generative AI and large language models (LLMs) and
looking to advance their machine learning engineering career

Key Features:

Spark, Kubernetes, and Apache Airflow
system design

Detect drift and build robust mechanisms into your solutions
monitoring

Distributed machine learning patterns, yuan tang
AWS and open-source tools

Book Description:

Machine Learning Engineering with Python, 2nd Edition, is the practical guide
Tableau your data!: fast and easy visual analysis with tableau software, daniel g. murray
What You Will Learn
Detect drift and build robust mechanisms into your solutions.

on ML. Youll take CI/CD further with continuous training and testing and go
Distributed machine learning patterns, yuan tang.
You'll go from understanding the key steps of the machine learning development
learning considerations regarding workflow, hardware, and scaling up
you've mastered the basics, you'll get hands-on with deployment architectures
workloads, as well as orchestrating workflows with Airlfow and Kafka. And take.

This edition goes deeper into ML engineering and MLOps, with a sharper focus
on ML. You'll take CI/CD further with continuous training and testing and go
in-depth into data and concept drift.

With a new generative AI chapter, explore Hugging Face, PyTorch, and GitHub
Copilot, and consume an LLM via an API using LangChain. You'll also cover deep
that MLOps and ML engineers need to build robust solutions to solve real-world
workloads, as well as orchestrating workflows with Airlfow and Kafka. And take
learning considerations regarding workflow, hardware, and scaling up
deployment further with canary, blue, and green deployments.

What You Will Learn:

With a new generative AI chapter, explore Hugging Face, PyTorch, and GitHub

Explore ANNs, DNNs, and LLMs, and get to grips with the rise of generative AI
in MLOps

Distributed machine learning patterns, yuan tang
Includes a new chapter on generative AI and large language models LLMs and

Explore ANNs, DNNs, and LLMs, and get to grips with the rise of generative AI

building a pipeline that leverages LLMs using LangChain

Supercharge your error handling with robust control flows and vulnerability
scanning

Distributed machine learning patterns, yuan tang

Who this book is for:

This book is designed for MLOps and ML engineers, data scientists, and
software developers who want to build robust solutions that use machine
learning to solve real-world problems. If you're not a developer but want to
manage or understand the product lifecycle of these systems, you'll also find
this book useful. It assumes a basic knowledge of machine learning concepts
and intermediate programming experience in Python. With its focus on practical
skills and real-world examples, this book is an essential resource for anyone
looking to advance their machine learning engineering career.

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