This instructor-led, live training in 台灣 (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process. By the end of this training, participants will be able to:
- Install and configure MLflow and related ML libraries and frameworks.
- Appreciate the importance of trackability, reproducability and deployability of an ML model
- Deploy ML models to different public clouds, platforms, or on-premise servers.
- Scale the ML deployment process to accommodate multiple users collaborating on a project.
- Set up a central registry to experiment with, reproduce, and deploy ML models.
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