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Key benefits

Become well-versed with MLOps techniques to monitor the quality of machine learning models in production

Explore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed models

Perform CI/CD to automate new implementations in ML pipelines

Description

Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you’ll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You’ll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you’ll apply the knowledge you’ve gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization.

Who is this book for?

This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book.

What you will learn

Formulate data governance strategies and pipelines for ML training and deployment

Get to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelines

Design a robust and scalable microservice and API for test and production environments

Curate your custom CD processes for related use cases and organizations

Monitor ML models, including monitoring data drift, model drift, and application performance

Build and maintain automated ML systems

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Frequently bought together

$48.99

Machine Learning Engineering with Python

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Machine Learning Engineering with MLflow

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Amazon verified reviews

Laurier common May 06, 2021

The author provides a high-level view of MLOps as well as detailed explanations pretty well which helps understand the concept well to someone who is a novice. The well-versed reader would enjoy the detailed implementations of the concepts. The author makes it simple to understand and I would highly recommend it to anyone who is interested in MLOps.

Amazon Verified review

Aditya Sen May 06, 2021

A nice view on practical data science and machine learning. Great book for newbies, some interesting views for seasoned practitioners. It gives a clear picture of MLOps and its components such as the ML pipeline, continuous deployment, security, continual learning, model monitoring, etc. The book has a good balance of theory and practical implementation, especially recommended for Data Scientists and ML Engineers, but has a bit of everything for anyone who works in the tech industry.

Paulo Carvalho May 11, 2021

I am a Data Scientist and wanted to learn MLOps for a long time but couldn't find a good resource. The information in MLOps is all scattered and confusing/conflicting on the internet. I generally found fluffy or theoretical books/resources. Now, finally found this book! It laid down a good foundation and case for MLOps and has hands-on implementation. I could work on the practical implementation of building, deploying and monitoring an ML model using a CI-CD pipeline and I am gradually feeling confident about MLOps now to implement it in my work. The book gave me new insights on ML testing, security, pipeline orchestration and monitoring.Recommend this book!

Kindle Customer May 23, 2021

Just finished reading this book and really enjoyed it. While the sample application throughout the book is simple, the author covers a lot of topics with both theories and hands-on experiments (in Azure) for all aspects of MLOps. Highly recommended.

Vatsal May 19, 2021

Really loved the explanation of the core concepts. The code snippets and walkthroughs really hammer in the concepts.

Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale

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