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.
Languages Available: English | 日本語 | Português BR | 한국어
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
Outline
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
Upcoming Public Classes
Date | Time | Your Local Time | Language | Price |
|---|---|---|---|---|
Jun 03 | 01 PM - 05 PM (Europe/London) | - | English | $750.00 |
Jun 05 | 08 AM - 12 PM (Asia/Kolkata) | - | English | $750.00 |
Jul 08 | 01 PM - 05 PM (Australia/Sydney) | - | English | $750.00 |
Jul 08 | 09 AM - 01 PM (America/New_York) | - | English | $750.00 |
Public Class Registration
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