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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

Skill Level
Associate
Duration
4h
Prerequisites

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
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

If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.

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Registration options

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Upcoming Public Classes

Data Engineer

DevOps Essentials for Data Engineering

This course explores software engineering best practices and DevOps principles, specifically designed for data engineers working with Databricks. Participants will build a strong foundation in key topics such as code quality, version control, documentation, and testing. The course emphasizes DevOps, covering core components, benefits, and the role of continuous integration and delivery (CI/CD) in optimizing data engineering workflows.

You will learn how to apply modularity principles in PySpark to create reusable components and structure code efficiently. Hands-on experience includes designing and implementing unit tests for PySpark functions using the pytest framework, followed by integration testing for Databricks data pipelines with Spark Declarative Pipeline and Jobs to ensure reliability.

The course also covers essential Git operations within Databricks, including using Databricks Git Folders to integrate continuous integration practices. Finally, you will take a high level look at various deployment methods for Databricks assets, such as REST API, CLI, SDK, and Declarative Automation Bundles (DABs), providing you with the knowledge of techniques to deploy and manage your pipelines.

By the end of the course, you will be proficient in software engineering and DevOps best practices, enabling you to build scalable, maintainable, and efficient data engineering solutions.

Languages Available: English | 日本語 | Português BR | 한국어 | Español | française

Paid
4h
Lab
instructor-led
Associate

Questions?

If you have any questions, please refer to our Frequently Asked Questions page.