Bringing your machine learning to production with ML Ops (sponsored by Microsoft)
Sarah Bird offers an overview of ML Ops (DevOps for machine learning), sharing solutions and best practices for an end-to-end pipeline for data preparation, model training, and model deployment while maintaining a comprehensive audit trail. Join in to learn how to build a cohesive and friction-free ecosystem for data scientists and app developers to collaborate together and maximize impact.
| Talk Title | Bringing your machine learning to production with ML Ops (sponsored by Microsoft) |
| Speakers | Sarah Bird (Microsoft) |
| Conference | O’Reilly Artificial Intelligence Conference |
| Conf Tag | Put AI to Work |
| Location | New York, New York |
| Date | April 16-18, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Creating an ML model is just a starting point. The challenge is getting the model deployed into a production environment and keeping it operational and supportable. Organizations need to manage the end to end lifecycle of code, data, models and applications and services—a task that spans multiple personas and multiple clouds. Sarah Bird offers an overview of ML Ops (DevOps for machine learning), sharing solutions and best practices for an end-to-end pipeline for data preparation, model training, and model deployment while maintaining a comprehensive audit trail. Join in to learn how to build a cohesive and friction-free ecosystem for data scientists and app developers to collaborate together and maximize impact.
