Achieving reproducibility in ML with a standard approach

Data collection is an imperfect science, and incredibly complex machine learning (ML) systems often lack specification – so in order to achieve reproducibility, experimentation is necessary, and must be standardized.
The reproducibility crisis in ML is a serious barrier to future progress for any organization. Read this e-book to:
- Learn the 7 steps in standardizing experimentation
- Identify the benefits and steps in achieving reproducibility
- Understand why the CACE principle matters
- And more
- Vendor:
- Comet
- Posted:
- Jun 15, 2022
- Published:
- Jun 15, 2022
- Format:
- Type:
- eBook