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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
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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Skills@Scale

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Upcoming Public Classes

Machine Learning Practitioner

Advanced Machine Learning with Databricks

This course is aimed at data scientists and machine learning practitioners and consists of two, four-hours modules. 

Machine Learning at Scale

In this course, you will gain theoretical and practical knowledge of Apache Spark’s architecture and its application to machine learning workloads within Databricks. You will learn when to use Spark for data preparation, model training, and deployment, while also gaining hands-on experience with Spark ML and pandas APIs on Spark. This course will introduce you to advanced concepts like hyperparameter tuning and scaling Optuna with Spark. This course will use features and concepts introduced in the associate course such as MLflow and Unity Catalog for comprehensive model packaging and governance.

Advanced Machine Learning Operations

In this course, you will be provided with a comprehensive understanding of the machine learning lifecycle and MLOps, emphasizing best practices for data and model management, testing, and scalable architectures. It covers key MLOps components, including CI/CD, pipeline management, and environment separation, while showcasing Databricks’ tools for automation and infrastructure management, such as Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving. You will learn about monitoring, custom metrics, drift detection, model rollout strategies, A/B testing, and the principles of reliable MLOps systems, providing a holistic view of implementing and managing ML projects in Databricks.

Paid
8h
Lab
instructor-led
Professional

Questions?

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