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Overview
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.
Model Deployment Fundamentals Batch Deployment Pipeline Deployment Machine Learning Model Deployment Design Document
At a minimum, you should be familiar with the following before attempting to take this content:
Coming Soon...
Model Deployment Fundamentals= Model Deployment Strategies Model Deployment with MLflow Batch Deployment Introduction to Batch Deployment Demo: Batch Deployment Lab: Batch Deployment Pipeline Deployment Introduction to Pipeline Deployment Demo: Pipeline Deployment Machine Learning Model Deployment Design Document Introduction to Real-time Deployment Databricks Model Serving Demo: Real-time Deployment with Model Serving Demo: Custom Model Deployment with Model Serving Lab: Real-time Deployment with Model Serving
Model Deployment Fundamentals=
Batch Deployment
Pipeline Deployment
Machine Learning Model Deployment Design Document
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