May 1, 2024
3 minute read
Machine Learning Deployment (ML Deployment) refers to the process of taking a trained machine learning model and making it available for use by end users. This involves packaging the model, deploying it to a runtime environment, and monitoring its performance. ML Deployment is a critical step in the machine learning workflow, as it enables models to be used to solve real-world problems.
Why Learn ML Deployment?
There are several reasons why one might want to learn about ML Deployment. These include:
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Find a path to becoming a ML Deployment. Learn more at:
OpenCourser.com/topic/j2agk2/ml
Reading list
We've selected 13 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
ML Deployment.
Focuses on the engineering aspects of ML Deployment, providing practical guidance on building and managing production-ready ML systems.
Covers the intersection of Machine Learning and DevOps, providing practical guidance on building and deploying production-ready ML systems.
Covers a wide range of ML topics, including a chapter on ML Deployment using popular Python libraries.
Provides a high-level overview of ML, including a discussion of ML Deployment.
Covers a wide range of ML topics, including a chapter on ML Deployment using R.
While this book covers AutoML, which subtopic of ML Deployment, it is highly relevant as it provides insights into the automation of the ML Deployment process.
Provides a theoretical foundation for ML, including a discussion of ML Deployment.
Covers Reinforcement Learning, a subfield of ML that is increasingly used in ML Deployment.
Covers Computer Vision applications of ML, including a chapter on ML Deployment for Computer Vision tasks.
Covers sparse statistical learning methods, which are often used in ML Deployment.
Explores the business applications of ML, including a chapter on ML Deployment.
This German-language book provides a broad overview of Machine Learning, including a chapter on ML Deployment.
Provides a theoretical foundation for convex optimization, which is used in many ML Deployment algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/j2agk2/ml
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