The Missing Piece of MLOps: Observability in Machine Learning Applications

The Missing Piece of MLOps: Observability in Machine Learning Applications

Explore the critical differences between MLOps and DevOps in this insightful conference talk from the Toronto Machine Learning Series. Delve into the unique challenges of MLOps and discover why observability is the key to unlocking its full potential. Learn how to prepare for ML observability and understand its role in showcasing the business value of deployed machine learning applications. Gain valuable insights from Marcelo Litovsky, Director of Sales Engineering at Aporia, as he discusses the importance of monitoring data changes in ML models and how observability can help data scientists and MLOps practitioners gain recognition for their work.