Machine Learning Model Deployment
This course is designed to introduce three primary machine learning deployment strategies and illustrate the implementation of each strategy on Databricks. Following an exploration of the fundamentals of model deployment, the course delves into batch inference, offering hands-on demonstrations and labs for utilizing a model in batch inference scenarios, along with considerations for performance optimization. The second part of the course comprehensively covers pipeline deployment, while the final segment focuses on real-time deployment. Participants will engage in hands-on demonstrations and labs, deploying models with Model Serving and utilizing the serving endpoint for real-time inference.
At a minimum, you should be familiar with the following before attempting to take this content:
Knowledge of fundamental machine learning models
Knowledge of model lifecycle and MLflow components
Familiarity with Databricks workspace and notebooks
Intermediate level knowledge of Python
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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