DP-3014 | Build machine learning solutions using Azure Databricks
 
Azure Databricks is a fully managed, cloud-based data analytics platform, which empowers developers to accelerate AI and innovation by simplifying the process of building enterprise-grade data applications. Built as a joint effort by Microsoft and the team that started Apache Spark, Azure Databricks provides data science, engineering, and analytical teams with a single platform for big data processing and machine learning. In this course, you’ll learn how to use Azure Databricks to train and deploy machine learning models.

Audience Profile
This course is designed for aspiring data scientists and AI engineers who need to train and manage machine learning models by using Azure Databricks.
 
Prerequisites
This learning path assumes that you have experience of using Python to explore data and train machine learning models with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow. Consider completing the Create machine learning models learning path before starting this one.

Role
AI Engineer
Data Scientist

Course Outline
Module 1: Explore Azure Databricks
Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
Introduction
Get started with Azure Databricks
Identify Azure Databricks workloads
Understand key concepts
Data governance using Unity Catalog and Microsoft Purview
Exercise - Explore Azure Databricks
Module assessment
Summary
Module 2: Use Apache Spark in Azure Databricks
Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.
Introduction
Get to know Spark
Create a Spark cluster
Use Spark in notebooks
Use Spark to work with data files
Visualize data
Exercise - Use Spark in Azure Databricks
Module assessment
Summary
Module 3: Train a machine learning model in Azure Databricks
Machine learning involves using data to train a predictive model. Azure Databricks support multiple commonly used machine learning frameworks that you can use to train models.
Introduction
Understand principles of machine learning
Machine learning in Azure Databricks
Prepare data for machine learning
Train a machine learning model
Evaluate a machine learning model
Exercise - Train a machine learning model in Azure Databricks
Module assessment
Summary
Module 4: Use MLflow in Azure Databricks
MLflow is an open source platform for managing the machine learning lifecycle that is natively supported in Azure Databricks.
Introduction
Capabilities of MLflow
Run experiments with MLflow
Register and serve models with MLflow
Exercise - Use MLflow in Azure Databricks
Module assessment
Summary
Module 5: Tune hyperparameters in Azure Databricks
Tuning hyperparameters is an essential part of machine learning. In Azure Databricks, you can use the Optune library to optimize hyperparameters automatically.
Introduction
Optimize hyperparameters with Optuna
Review trials
Scale hyperparameter optimization
Exercise - Optimize hyperparameters for machine learning in Azure Databricks
Module assessment
Summary
Module 6: Use AutoML in Azure Databricks
AutoML in Azure Databricks simplifies the process of building an effective machine learning model for your data.
Introduction
What is AutoML?
Use AutoML in the Azure Databricks user interface
Use code to run an AutoML experiment
Exercise - Use AutoML in Azure Databricks
Module assessment
Summary
Module 7: Train deep learning models in Azure Databricks
Deep learning uses neural networks to train highly effective machine learning models for complex forecasting, computer vision, natural language processing, and other AI workloads.
Introduction
Understand deep learning concepts
Train models with PyTorch
Distribute PyTorch training with TorchDistributor
Exercise - Train deep learning models on Azure Databricks
Module assessment
Summary
Module 8: Manage machine learning in production with Azure Databricks
Machine learning enables data-driven decision-making and automation, but deploying models into production for real-time insights is challenging. Azure Databricks simplifies this process by providing a unified platform for building, training, and deploying machine learning models at scale, fostering collaboration between data scientists and engineers.
Introduction
Automate your data transformations
Explore model development
Explore model deployment strategies
Explore model versioning and lifecycle management
Exercise - Manage a machine learning model
Module assessment
Summary
 
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