Successfully building a machine learning model is hard enough. Reproducing your results at scale — enabling others to reproduce pipelines, comparing results from other versions, moving models into production, redeploying and rolling out updated models — is exponentially harder. To address these challenges and accelerate innovation, many companies are building custom “ML platforms” to automate the end-to-end ML lifecycle.
Join our interactive MLOps Virtual Event to hear more about the latest developments and best practices for managing the full ML lifecycle on Databricks with MLflow. We will cover a checklist of capabilities you’ll need, common pitfalls, technological and organizational challenges, and how to overcome them.
Presentations will be enhanced with demos, as well as success stories and learnings from experts who have deployed real-world examples for forecasting, IoT analytics and more. Live Q&As and discussions will keep this event engaging for data science leaders and practitioners alike.
To watch the On-Demand Video please click here.