DP-3014 Build machine learning solutions using Azure Databricks

Course DP-3014 Build machine learning solutions using Azure Databricks

Azure Databricks is a cloud-scale platform for data analytics and machine learning. Data scientists and machine learning engineers can use Azure Databricks to deploy large-scale machine learning solutions.

Level: Intermediate - Role: Data Scientist - Product: Azure Databricks - Subject: Artificial Intelligence, Data Management, Machine Learning

DP-3014 Training Objectives

• Explore how Azure Databricks enables data scientists and machine learning engineers to deploy large-scale machine learning solutions.
• Leverage Azure Databricks' cloud scaling capabilities to train and deploy machine learning models.
• Use open-source tools and frameworks, such as Scikit-Learn, PyTorch, and TensorFlow, in the Azure Databricks environment.
• Use open-source tools and frameworks, such as Scikit-Learn, PyTorch, and TensorFlow, in the Azure Databricks environment.
• Optimize and manage machine learning models for advanced data analysis and effective predictions.
• Develop hands-on experience implementing machine learning workflows in an Azure Databricks environment.

Course Content DP-3014: Build Machine Learning Solutions with Azure Databricks

Module 1: Explore Azure Databricks

• Introduction to Azure Databricks
• Identifying Azure Databricks Workloads
• Description of key concepts
• Exercise: Explore Azure Databricks

Module 2: Using Apache Spark on Azure Databricks

• Discover Spark
• Creating a Spark Cluster
• Using Spark in Notebooks
• Using Spark to work with data files
• Data visualization
• Exercise: Using Spark in Azure Databricks

Module 3: Training a Machine Learning Model in Azure Databricks

• Description of the principles of machine learning
• Machine learning in Azure Databricks
• Preparing data for machine learning
• Training a Machine Learning Model
• Evaluate a Machine Learning Model
• Exercise: Training a Machine Learning Model in Azure Databricks

Module 4: Using MLflow in Azure Databricks

• MLflow Features
• Running experiments with MLflow
• Model Registration and Serving with MLflow
• Exercise: Using MLflow in Azure Databricks

Module 5: Hyperparameter Tuning in Azure Databricks

• Hyperparameter Optimization with Hyperopt
• Hyperopt Test Review
• Hyperopt Test Scale
• Exercise: Hyperparameter Optimization for Machine Learning in Azure Databricks

Module 6: Using AutoML in Azure Databricks

• What is AutoML?
• Using AutoML in the Azure Databricks UI
• Using code to run an AutoML experiment
• Exercise: Using AutoML in Azure Databricks

Module 7: Training Deep Learning Models in Azure Databricks

• Understanding Deep Learning Concepts
• Model training with PyTorch
• PyTorch training distribution with Horovod
• Exercise: Training Deep Learning Models in Azure Databricks

Module 8: Manage machine learning in production with Azure Databricks

• Introduction
  
• Automate your data transformations
  
• Explore model development
  
• Explore model implementation strategies
  
• Explore model versioning and lifecycle management
  
• Exercise: Managing a Machine Learning Model

Prerequisites

Experience using Python to explore data and train machine learning models with common open-source frameworks such as S cikit-Learn, PyTorch, and TensorFlow is recommended.

Language

• Course: English / Spanish
• Labs: English / Spanish