HomepageData + AI Summit 2022 Logo
Watch on demand

MLflow Pipelines: Accelerating MLOps from Development to Production

On Demand

Type

  • Session

Format

  • Hybrid

Track

  • MLOps and DataOps

Room

  •  Moscone South | Level 2 | 215

Duration

  • 35 min
Download session slides

Vue d'ensemble

Despite being an emerging topic, MLOps is hard and there are no widely established approaches for MLOps. What makes it even harder is that in many companies the ownership of MLOps usually falls through the cracks between data science teams and production engineering teams. Data scientists are mostly focused on modeling the business problems and reasoning about data, features, and metrics, while the production engineers/ops are mostly focused on traditional DevOps for software development, ignoring ML-specific Ops like ML development cycles, experiment tracking, data/model validation, etc.
In this talk, we will introduce MLflow Pipelines, an opinionated approach for MLOps. It provides predefined ML pipeline templates for common ML problems and opinionated development workflows to help data scientists bootstrap ML projects, accelerate model development, and ship production-grade code with little help from production engineers.

Session Speakers

Jin Zhang

Databricks

Xiangrui Meng

Databricks

Visionnez les temps forts du Data+AI Summit

Watch on demand