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:
- Familiarity with the Databricks Data Intelligence Platform and basic workspace operations (create clusters, run code in notebooks, use basic notebook operations, import repos from git)
- Intermediate programming experience with Python, including data manipulation libraries (pandas, numpy) and working with APIs (databricks-sdk, REST endpoints)
- Basic knowledge of MLflow for experiment tracking, model logging, model registry operations, and model versioning
- Understanding of machine learning fundamentals, including model training, evaluation, batch inference, and real-time deployment concepts
- Intermediate experience with Unity Catalog for data governance and model registry management
- Basic familiarity with Feature Engineering concepts, including feature tables, feature lookups, and offline vs online feature stores
- Understanding of Delta Lake operations (create tables, perform updates, optimize files, and liquid clustering) and data storage optimization techniques
- Basic knowledge of Apache Spark and PySpark for distributed data processing and User Defined Functions (UDFs)
Outline
1. Model Deployment Fundamentals
• Model Deployment Strategies
• Model Deployment with MLflow
2. Batch Deployment
• Introduction to Batch Deployment
• Demo: Batch Deployment
• Lab: Batch Deployment
3. Pipeline Deployment
• Introduction to Pipeline Deployment
• Demo: Pipeline Deployment
4. Real-time Deployment and Online Stores
• 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 |
|---|---|---|---|---|
Oct 28 | 09 AM - 01 PM (Asia/Kolkata) | - | English | $750.00 |
Dec 03 | 09 AM - 01 PM (Asia/Kolkata) | - | English | $750.00 |
Dec 03 | 01 PM - 05 PM (Europe/Paris) | - | English | $750.00 |
Dec 03 | 09 AM - 01 PM (America/New_York) | - | English | $750.00 |
Jan 07 | 09 AM - 01 PM (Asia/Singapore) | - | English | $750.00 |
Jan 07 | 01 PM - 05 PM (America/New_York) | - | English | $750.00 |
Jan 08 | 09 AM - 01 PM (Europe/Paris) | - | 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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