Designing ML Systems - Model Development and Offline Evaluation

Model development is an iterative process. After each iteration, you’ll want to compare your model’s performance against its performance in previous iterations and evaluate how suitable this iteration is for production.
To build an ML model, we first need to select the ML model to build. There are so many ML algorithms out there, with more actively being developed.
In this video, we will discusses different aspects of model development, such as debugging, experiment tracking and versioning, and distributed training.