Last week, I attended Comet ML’s Convergence virtual event. The event features presentations from data science and machine learning experts, who shared their best practices and insights on developing and implementing enterprise ML strategies. There were talks discussing emerging tools, approaches, and workflows that can help you effectively manage an ML project from start to finish.

In this blog recap, I will dissect content from the event’s technical talks, covering a wide range of topics from testing models in production and data quality assessment to operational ML and minimum viable model.