Ultimate MLOps for Machine Learning Models : Use Real Case Studies to Efficiently Build, Deploy, and Scale Machine Learning Pipelines with MLOps (English Edition).

Format:

Book

Author/Creator:

Dorle, Saurabh D.

Language:

English

Subjects (All):

Machine learning.

Information technology.

Physical Description:

1 online resource (219 pages)

Edition:

1st ed.

Place of Publication:

Delhi : Orange Education PVT Ltd, 2024.

Summary:

This book is an essential resource for professionals aiming to streamline and optimize their machine learning operations. This comprehensive guide provides a thorough understanding of the MLOps life cycle, from model development and training to deployment and monitoring. By delving into the intricacies of each phase, the book equips readers with the knowledge and tools needed to create robust, scalable, and efficient machine learning workflows. Key chapters include a deep dive into essential MLOps tools and technologies, effective data pipeline management, and advanced model optimization techniques. The book also addresses critical aspects such as scalability challenges, data and model governance, and security in machine learning operations. Each topic is presented with practical insights and real-world case studies, enabling readers to apply best practices in their job roles. Whether you are a data scientist, ML engineer, or IT professional, this book empowers you to take your machine learning projects from concept to production with confidence. It equips you with the practical skills to ensure your models are reliable, secure, and compliant with regulations. By the end, you will be well-positioned to navigate the ever-evolving landscape of MLOps and unlock the true potential of your machine learning initiatives.

Contents:

Cover Page

Title Page

Copyright Page

Dedication Page

About the Author

About the Technical Reviewer

Acknowledgements

Preface

Errata

Table of Contents

1. Introduction to MLOps

Introduction

Structure

Introduction to Machine Learning

Types of Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Applications of Machine Learning

Rise of Machine Learning

Early Beginnings

Knowledge-Based System

Statistics and Neural Networks

Big Data

Modern Era

Challenges of Deploying and Managing ML Models in Production

Data Drift

Model Explainability

Infrastructure and Scalability

Security and Privacy Generated by AI.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9788197651205

8197651205

OCLC:

1455116718