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Machine Learning Operations

This course will guide participants through a comprehensive exploration of machine learning model operations, focusing on MLOps and model lifecycle management. The initial segment covers essential MLOps components and best practices, providing participants with a strong foundation for effectively operationalizing machine learning models. In the latter part of the course, we will delve into the basics of the model lifecycle, demonstrating how to navigate it seamlessly using the Model Registry in conjunction with the Unity Catalog for efficient model management. By the course's conclusion, participants will have gained practical insights and a well-rounded understanding of MLOps principles, equipped with the skills needed to navigate the intricate landscape of machine learning model operations.


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 concepts of machine learning
  • Knowledge of MLflow tracking
  • Familiarity with Databricks workspace and notebooks
  • Intermediate level knowledge of Python

Outline

Modern MLOps
Defining MLOps
MLOps on Databricks
Working with Asset Bundles

Architecting MLOps Solutions

Introduction 
Opinionated MLOps Principles
Recommended MLOps Architectures
Model Testing Job with the Databricks CLI

Implementation and Monitoring MLOps Solution

Introduction 
Implementation of MLOps Stacks
Type of Model Monitoring
Monitoring in Machine Learning
Lakehouse Monitoring Dashboard

Upcoming Public Classes

Date
Time
Language
Price
Mar 27
09 AM - 01 PM (Asia/Kolkata)
English
$750.00
Mar 27
01 PM - 05 PM (America/New_York)
English
$750.00
Mar 28
09 AM - 01 PM (Europe/London)
English
$750.00
Apr 17
09 AM - 01 PM (Europe/London)
English
$750.00
Apr 22
09 AM - 01 PM (America/New_York)
English
$750.00
May 05
01 PM - 05 PM (Europe/London)
English
$750.00
May 06
09 AM - 01 PM (Australia/Sydney)
English
$750.00
May 14
01 PM - 05 PM (America/New_York)
English
$750.00
Jun 05
09 AM - 01 PM (Asia/Singapore)
English
$750.00
Jun 09
09 AM - 01 PM (Europe/London)
English
$750.00
Jun 18
09 AM - 01 PM (America/New_York)
English
$750.00
Jul 04
09 AM - 01 PM (Asia/Kolkata)
English
$750.00
Jul 07
01 PM - 05 PM (Europe/London)
English
$750.00
Jul 18
01 PM - 05 PM (America/New_York)
English
$750.00

Public Class Registration

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Registration options

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Upcoming Public Classes

Data Engineer

Automated Deployment with Databricks Asset Bundles

This course provides a comprehensive review of DevOps principles and their application to Databricks projects. It begins with an overview of core DevOps, DataOps, continuous integration (CI), continuous deployment (CD), and testing, and explores how these principles can be applied to data engineering pipelines.

The course then focuses on continuous deployment within the CI/CD process, examining tools like the Databricks REST API, SDK, and CLI for project deployment. You will learn about Databricks Asset Bundles (DABs) and how they fit into the CI/CD process. You’ll dive into their key components, folder structure, and how they streamline deployment across various target environments in Databricks. You will also learn how to add variables, modify, validate, deploy, and execute Databricks Asset Bundles for multiple environments with different configurations using the Databricks CLI.

Finally, the course introduces Visual Studio Code as an Interactive Development Environment (IDE) for building, testing, and deploying Databricks Asset Bundles locally, optimizing your development process. The course concludes with an introduction to automating deployment pipelines using GitHub Actions to enhance the CI/CD workflow with Databricks Asset Bundles.

By the end of this course, you will be equipped to automate Databricks project deployments with Databricks Asset Bundles, improving efficiency through DevOps practices.

Paid
4h
Lab
instructor-led
Professional

Questions?

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