Research shows that one of the top challenges faced by data scientists and machine learning (ML) engineers is reproducibility, or the ability to run an algorithm on particular datasets with similar results. If you are part of a mid-market or enterprise team looking to advance your use of ML, reproducibility can be a barrier to ensuring positive outcomes and scaling great work.
In this webinar, you will learn about:
- The four aspects of reproducibility in machine learning
- A five-point reproducibility checklist you can use to ensure reproducibility in ML experimentation across your organization
- How a machine learning platform can assist with ML reproducibility