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.
End-to-end MLOps for PyTorch on Databricks using MLflow — Sean Owen, Principal Architect, Databricks.Watch now
Applying MLOps at Scale — Keven Wang, Competence Lead, ML Engineer, H&M – an overview of H&M reference architecture and demo of the production workflow. This talk will cover the entire MLOps stack addressing a few common challenges in AI and Machine learning products, like development efficiency, end to end traceability, speed to production, etc.Watch now | Slides
CI/CD in MLOps - Implementing a Framework for Self-Service Experimentation and Deployment at Enterprise Scale —Wesly Clark, Chief Architect, Enterprise Analytics and AI, J.B. Hunt Transport and Cara Phillips, Data Science, MLOps Consultant, Artis Consulting – this talk will cover the core values, concepts, and conventions of the framework followed by a technical demo of how to implement the self-service automation of Databricks resources, code, and jobs deployment into Azure DevOps CI/CD pipelines.Watch now | Slides
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.Watch now
A collection of data science and machine learning talks from leading industry experts from Atlassian, Zynga, Starbucks, and more.