We all know that machine learning is iterative, complex, and — at times — messy. Managing your experiments and automating the most tedious parts of the ML process are often the best place to start when it comes simplifying and making your job easier. This session will explore how experiment management can help you:
- Automatically track datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility – all at scale
- Standardize and track experiments across data science teams, even in large organizations
- Deliver custom visualizations and reports, to clearly communicate the results of your research
