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.
The field is dynamic and rapidly evolving, offering a unique blend of software engineering, data analysis, and system architecture. For those fascinated by how theoretical models translate into real-world impact, a career in MLOps can be incredibly engaging. It involves building the "factories" that produce and manage AI, ensuring that machine learning models are not just one-off projects but are scalable, reliable, and continuously improving assets. This intersection of robust engineering and cutting-edge AI makes MLOps a compelling path for problem-solvers who enjoy building resilient systems.
Introduction to MLOps
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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).
Offers a holistic approach to designing ML systems, emphasizing reliability, scalability, and maintainability. It discusses various design decisions throughout the ML lifecycle, from data processing to monitoring. It's a strong choice for those who need to architect and build end-to-end ML solutions.
Foundational work that is referenced in a multitude of ML works. This book provides a deep look at the math behind ML.
Provides a solid introduction to the core concepts of MLOps. It covers the ML model life cycle, including building, preproduction, deployment, monitoring, and governance. This is an excellent starting point for anyone looking to understand the 'what' and 'why' of MLOps before diving into technical details.
Presents a collection of design patterns for various stages of the ML lifecycle, with a significant focus on MLOps. It offers proven solutions to common problems encountered when building and deploying ML systems. This practical guide for developers and engineers.
Focusing on practical implementation, this book guides readers through the process of operationalizing ML models. It provides hands-on examples and best practices for building robust MLOps pipelines. This valuable resource for those who want to move beyond theoretical understanding and apply MLOps principles.
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.
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.
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.
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 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.
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 much more than just MLOps. For developers wanting to gain an understanding of the broad aspects of AI, this great 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.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/608jke/machine
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