MLOps | Virtual Event
Operationalizing machine learning at scale
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.
What's Next?
Free Tutorial: Introducing MLflow on Databricks
In this simple hands-on tutorial, we’ll take a look at how health data can be used to predict life expectancy. It will start with data engineering in Apache Spark, data exploration, model tuning and logging with hyperopt and MLflow. It will continue with examples of how the model registry governs model promotion, and simple deployment to production with MLflow as a job or dashboard.
Ready to start?
An open and unified data analytics platform for data engineering, machine learning and analytics
