Successfully building a machine learning model is hard enough. Tracking thousands of experiments, reproducing results at scale, moving models into production, redeploying and rolling out updated models is exponentially harder. To address these challenges, many organizations are building custom “ML platforms” to automate and standardize the end-to-end ML lifecycle.
Watch our talks below to learn more about the latest developments and best practices for building ML platforms, MLOps, and how managing and standardizing the full ML lifecycle on Databricks with MLflow can help organizations solve these common challenges and accelerate innovation.
