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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
Your Local Time
Language
Price
Jun 05
01 PM - 05 PM (Europe/London)
-
English
$750.00
Jun 10
08 AM - 12 PM (Asia/Kolkata)
-
English
$750.00
Jul 10
01 PM - 05 PM (Australia/Sydney)
-
English
$750.00
Jul 10
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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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.