A Guide to Implementing MLOps

From Data to Operations

By: Prafful Mishra

Write A Review

eText | 1 February 2025

At a Glance

Format
ePUB

eText

$64.99

or 4 interest-free payments of $16.25 with

Instant online reading in your Booktopia eTextbook Library *

Read online on

Desktop

Tablet

Mobile

Not downloadable to your eReader or an app

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production.