Machine Learning Operations (MLOps)
May 1, 2024
Updated June 28, 2025
18 minute read
Machine Learning Operations (MLOps)
Machine Learning Operations, or MLOps, is a discipline that combines machine learning, data engineering, and DevOps principles to automate and streamline the end-to-end machine learning lifecycle. At its core, MLOps aims to solve a significant challenge: the gap between creating a machine learning model and successfully running it in a live production environment. Many models that perform well in a lab setting fail to deliver value because deploying, monitoring, and maintaining them is a complex, manual, and often disjointed process. MLOps introduces the rigor and reliability of software engineering to the experimental world of data science.
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Find a path to becoming a Machine Learning Operations (MLOps). Learn more at:
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Reading list
We've selected 27 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning Operations (MLOps).
Foundational work that is referenced in a multitude of ML works. This book provides a deep look at the math behind ML.
From the author of 'The Hundred-Page Machine Learning Book,' this book focuses on the engineering aspects of building ML solutions. It covers best practices and design patterns for creating reliable and scalable ML systems. This practical guide for anyone involved in putting ML models into production.
Feature stores are a critical component in modern MLOps infrastructure for managing and serving features consistently. dives into the concepts and practicalities of using feature stores, which is highly relevant for building efficient and scalable ML pipelines.
Delves into building scalable MLOps systems, particularly leveraging cloud platforms like AWS. It's suitable for engineers looking to implement MLOps in a production environment and understand the infrastructure considerations. The book covers topics like data loading, model training deployment, and monitoring at scale.
Geoffrey Hinton's work with neural networks, deep learning, and ML spans over 40 years. provides the technical approach to AI in the real world.
Focuses on building the infrastructure that enables effective data science and MLOps. It covers topics like data storage, computation, and orchestration, drawing on practices from companies like Netflix. It's particularly useful for platform engineers and those designing ML infrastructure.
Specifically addresses the construction of ML pipelines using TensorFlow Extended (TFX). It's a hands-on guide to automating the various steps of the ML model lifecycle, a key aspect of MLOps. It's highly relevant for practitioners working with TensorFlow.
For those using or planning to use Kubeflow for their MLOps workflows, this book comprehensive guide. It covers the various components of Kubeflow and how they can be used to build, train, and deploy ML models on Kubernetes. It's a technical deep dive into a popular MLOps platform.
Focuses on Reinforcement Learning, a subset of ML. For those wanting to dive deeper into ML there is no better resource.
Covers ML concepts through a variety of tools and libraries like Scikit-Learn, Keras, and TensorFlow. This will help you apply machine learning algorithms to your real world problems.
While not strictly an MLOps book, this foundational text for anyone building data-intensive systems, which includes ML systems. It provides a deep understanding of the underlying principles of data systems, crucial for building scalable and reliable MLOps infrastructure. This valuable reference for experienced engineers.
Building on the previous recommendation, this book provides a dive into TensorFlow, a framework that allows for the creation of and training of ML models. This great resource for building deep neural networks, a key component of MLOps.
Addresses the practical challenges of managing machine learning models in production. It covers aspects like versioning, deployment, and monitoring from a logistical perspective, which core concern in MLOps.
MLOps shares many principles with Site Reliability Engineering (SRE), particularly concerning the operation and monitoring of production systems. offers valuable insights into building reliable and scalable services, directly applicable to the production phase of MLOps.
Covers much more than just MLOps. For developers wanting to gain an understanding of the broad aspects of AI, this great choice.
A strong foundation in data engineering is essential for MLOps. covers building data pipelines, working with various data sources, and automating data workflows using Python. It's a crucial prerequisite for understanding the data aspects of MLOps.
Covers deep learning which subset of ML. For developers familiar with MLOps who want to deep dive into how to build deep learning models this would be an excellent choice.
Understanding the business context and data-analytic thinking is fundamental to successful MLOps. provides a strong foundation in data science principles and how they apply to solving business problems. It helps bridge the gap between data scientists and business stakeholders.
While not directly about MLOps, this classic programming book instills crucial software engineering principles that are highly applicable to building robust and maintainable ML systems. Its focus on practical advice and good practices makes it valuable for any MLOps professional.
Writing clean and maintainable code is vital for collaborative MLOps environments. provides timeless principles for writing understandable and flexible code, which directly contributes to the success of MLOps practices.
This classic in the field of statistical learning and data mining. It provides a rigorous foundation in the theoretical underpinnings of many machine learning techniques. While not an MLOps book, it offers essential background knowledge for those seeking a deep understanding of ML.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/608jke/machine
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