Get Started with Databricks for Machine Learning

In this course, you will develop the foundational skills needed to use the Databricks Data Intelligence Platform for executing basic machine learning workflows and supporting data science workloads. You will explore the platform from the perspective of a machine learning practitioner, covering topics such as feature engineering with Databricks Notebooks and model lifecycle tracking with MLflow. Additionally, you will learn about real-time model inference with Mosaic AI Model Serving and experience Databricks’ “glass box” approach to model development through AutoML. The course includes three instructor-led demonstrations, culminating in a comprehensive lab that reinforces the concepts covered in the demos.

Skill Level

Onboarding

Duration

Prerequisites

A beginner-level understanding of Python.
Basic understanding of DS/ML concepts (e.g. classification and regression models), common model metrics (e.g. F1-score), and Python libraries (e.g. scikit-learn and XGBoost).

Outline

Databricks Overview

Databricks Infrastructure
Databricks Data Intelligence Platform
Unity Catalog Overview
Databricks Workspace Walkthrough

Using Databricks for Machine Learning

Introduction to Machine Learning on Databricks
Exploratory Data Analysis (EDA) and Feature Engineering on Databricks
Introduction to Mosaic AI AutoML
Introduction to MLflow on Databricks
Introduction to Mosaic AI Model Serving

Upcoming Public Classes

Date	Time	Language	Price
Date	Time	Language	Price	Jan 16	02 PM - 04 PM (America/New_York)	English	Free
Jan 23	09 AM - 11 AM (Europe/London)	English	Free
Jan 27	12 PM - 02 PM (Asia/Singapore)	English	Free
Feb 12	02 PM - 04 PM (Asia/Kolkata)	English	Free
Feb 12	02 PM - 04 PM (Europe/London)	English	Free
Feb 13	02 PM - 06 PM (Asia/Singapore)	English	Free
Feb 13	09 AM - 11 AM (America/Los_Angeles)	English	Free
Feb 13	12 PM - 02 PM (America/Los_Angeles)	English	Free
Feb 17	03 PM - 05 PM (Europe/London)	English	Free
Feb 20	02 PM - 04 PM (Asia/Kolkata)	English	Free
Feb 28	12 PM - 02 PM (Asia/Singapore)	English	Free

Public Class Registration

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Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Machine Learning Practitioner

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.

Note: This course is the first in the series of Advanced Machine Learning.

Free

Professional

Generative AI Engineer

Generative AI Fundamentals

Welcome to Generative AI Fundamentals. This course provides an introduction to how organizations can understand and utilize generative artificial intelligence (AI) models. First, we'll start off with a quick introduction to generative AI - we'll discuss what it is and pay special attention to large language models, also known as LLMs. Then, we’ll move into how organizations can find success with generative AI - we’ll take a deeper dive into what LLM applications are, discuss how Lakehouse AI can help you succeed, and discuss essential considerations for adopting AI in general. Finally, we'll tackle important aspects to consider when evaluating the potential risks and challenges associated with using/adopting generative AI.

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

Free

Introductory

Generative AI Engineer

Generative AI Application Development

The course is designed to provide you the practical experience in building advanced LLM applications using multi-stage reasoning LLM chains and agents. First, you’ll learn how to decompose a problem into its components and select the most suitable model for each step to enhance business use cases. Following this, we’ll show you how to construct a multi-stage reasoning chain utilizing LangChain and HuggingFace transformers. Finally, you’ll be introduced to agents and will design an autonomous agent using generative models on Databricks.

Note: This is the second course in the 'Generative AI Engineering with Databricks’ series.

Free

Associate