Practical MLOps: Operationalizing Machine Learning Models

MLOps is a set of proven principles for solving the challenge of getting machine learning models into production in a reliable and automated way. This guide covers what MLOps is, how it differs from DevOps, and how to implement it in AWS, Microsoft Azure, and Google Cloud. It also covers best practices for building production machine learning systems, monitoring, instrumenting, load-testing, and operationalizing them, and choosing the correct MLOps tools for a given task.

\n Format: Paperback / softback
\n Length: 450 pages
\n Publication date: 01 October 2021
\n Publisher: O'Reilly Media, Inc, USA
\n

Machine learning (ML) is a rapidly evolving field that has the potential to revolutionize various industries, from healthcare to finance. However, getting ML models into production is a fundamental challenge that requires a reliable and automated approach. This is where MLOps comes into play.

MLOps is a set of proven principles and practices that aim to solve the challenges of deploying and managing ML models in production environments. It combines elements of software development, data engineering, and machine learning to streamline the process of building, training, and deploying ML models.

One of the key differences between MLOps and DevOps is that MLOps focuses specifically on the unique challenges of machine learning, such as data processing, model training, and model deployment. MLOps tools and methods include AutoML, monitoring and logging, and containerization, which are designed to automate the ML workflow and improve efficiency.

In this comprehensive guide, we will explore what MLOps is, how it differs from DevOps, and how to implement it in AWS, Microsoft Azure, and Google Cloud. We will cover the essential MLOps tools and methods, including AutoML, monitoring and logging, and containerization, and show you how to apply them in real-world scenarios.

By the end of this guide, you will have a solid foundation in MLOps tools and methods and will be able to build production machine learning systems and maintain them. You will also be able to monitor, instrument, load-test, and operationalize machine learning systems, ensuring that they are reliable and performant.

Whether you are a current or aspiring machine learning engineer, or anyone familiar with data science and Python, this guide will give you a head start in MLOps. You will discover how to apply DevOps best practices to machine learning, build production machine learning systems, and maintain them. You will also learn how to monitor, instrument, load-test, and operationalize machine learning systems, ensuring that they are reliable and performant.

So, if you are ready to take your ML models to the next level and deploy them in production, this guide is for you. Let's get started!

\n Weight: 810g\n
Dimension: 177 x 234 x 30 (mm)\n
ISBN-13: 9781098103019\n \n