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 Strategies
- Model Deployment with MLflow
- Introduction to Batch Deployment
- Introduction to Pipeline Deployment
- Introduction to Real-time Deployment
- Databricks Model Serving
Upcoming Public Classes
Date | Time | Language | Price |
|---|---|---|---|
May 05 | 09 AM - 01 PM (Europe/London) | English | $750.00 |
May 06 | 01 PM - 05 PM (America/New_York) | English | $750.00 |
May 07 | 09 AM - 01 PM (Asia/Kolkata) | English | $750.00 |
Jul 07 | 01 PM - 05 PM (America/New_York) | English | $750.00 |
Jul 08 | 01 PM - 05 PM (Asia/Kolkata) | English | $750.00 |
Jul 09 | 01 PM - 05 PM (Europe/London) | English | $750.00 |
Public Class Registration
If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.
Private Class Request
If your company is interested in private training, please submit a request.
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