ML Deployment

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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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Machine Learning with TensorFlow, Second Edition

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Focuses on the engineering aspects of ML Deployment, providing practical guidance on building and managing production-ready ML systems.

Python Machine Learning

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Covers the intersection of Machine Learning and DevOps, providing practical guidance on building and deploying production-ready ML systems.

Hands-On Machine Learning with Scikit-Learn, Keras,...

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Covers a wide range of ML topics, including a chapter on ML Deployment using popular Python libraries.

Grokking Deep Learning

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Provides a high-level overview of ML, including a discussion of ML Deployment.

Machine Learning with R

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Covers a wide range of ML topics, including a chapter on ML Deployment using R.

Reinforcement Learning, second edition

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Covers Reinforcement Learning, a subfield of ML that is increasingly used in ML Deployment.

Energy Minimization Methods in Computer Vision and...

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Covers Computer Vision applications of ML, including a chapter on ML Deployment for Computer Vision tasks.

Statistical Learning with Sparsity

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Covers sparse statistical learning methods, which are often used in ML Deployment.

Convex Optimization

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Provides a theoretical foundation for convex optimization, which is used in many ML Deployment algorithms.

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ML Deployment

Why Learn ML Deployment?

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