Machine learning projects often rely on many different libraries and tools. To manage these dependencies, we need to have a streamlined build process. A fast and effective build process can make or break many projects. If you wait too long on builds, developer productivity suffers and projects get delayed. In this course, you'll learn the fundamentals of building efficient and effective build processes for your Java machine learning applications. You'll explore common build tools like Maven and Gradle, understanding how they can construct a build process. From here, you'll explore different optimizations for build processes, including caching, parallelization, automations, and multi-project builds.

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Recommended experience
What you'll learn
Evaluate which Java build tools best fit their projects.
Construct build processes in Maven and Gradle with optimized cachine and parallelism.
Implement common build tasks such as dependency resolution, build automation, and multi-project builds.
Skills you'll gain
Details to know

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December 2025
1 assignment
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There are 3 modules in this course
This module gives an overview of the Java build process, discussing common build tools and some of the tradeoffs between common tools.
What's included
4 videos2 readings1 peer review
This module explores the process of creating build processes with Gradle and Maven, focusing on machine learning based dependencies. We'll also explore optimization of build processes, including caching and parallelization.
What's included
3 videos1 reading1 peer review
In this module, we explore how to further extend builds to be as efficient and automated as possible. We'll focus primarily on dependency resolution, build automation through pipelines and breaking down builds into multiple projects.
What's included
4 videos1 reading1 assignment2 peer reviews
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