Part 11/12:
Existing large codebases—migrating may be complex.
Projects where speed is less critical than quick iteration or ease of use.
Summary and Resources
In conclusion, JAX offers a compelling package for performance-critical machine learning workflows. Its ability to seamlessly compile code, differentiate functions, and utilize hardware accelerators effectively makes it highly desirable for research and production.
The session wrapped up with recommended resources like Nicholas Frosen’s tutorials and official documentation, encouraging participants to explore and integrate JAX into their projects.