Intelligent Model Deployment and Consumer Pipeline With DataRobot AI Cloud Platform

Explore the intricacies of MLOps with a focus on model deployment, model registry, and model inference applications using the DataRobot AI Cloud Platform. Dive into deploying models from machine learning leaderboards into MLOps pipelines and learn various consumption methods including API, Stream, local mode, and job workflows. Gain insights into the DataRobot AI Cloud's unified environment designed for continuous optimization across the entire AI lifecycle, catering to data science experts, IT teams, and executives alike. Follow along as the tutorial covers model deployment options, creating applications for models, replacing deployed models, and various prediction methods, including CLI and workflow jobs. Benefit from a comprehensive overview of intelligent model deployment and consumer pipeline processes in this 43-minute video tutorial.

- Video Start
- Video Content Intro
- Earlier or prerequisite Video Tutorial
- Visiting Model Leaderboard
- Model Deployment Options
- Model Registry
- Let's Deploy Model
- Deploy the leader model
- Rebuild Model for Deployment
- Creating applications for Model
- Consuming application
- Deploy another Model
- Replacing Deployed Model
- Various Prediction Methods
- Prediction over CLI
- Prediction workflow Job
- Recap
- Thanks
- Credits