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Build machine learning solutions using Azure Databricks
Duration
1
Course
MOC-DP-3014
Available Formats
Classroom Training, Online Training
Course Description
Overview
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.Objectives
After completing this course, students will be able to:
- Provision an Azure Databricks workspace
- Identify core workloads for Azure Databricks
- Use Data Governance tools Unity Catalog and Microsoft Purview
- Describe key concepts of an Azure Databricks solution
- Describe key elements of the Apache Spark architecture.
- Create and configure a Spark cluster.
- Describe use cases for Spark.
- Use Spark to process and analyze data stored in files.
- Use Spark to visualize data.
- Use the AutoML user interface in Azure Databricks
- Use the AutoML API in Azure Databricks
- Automate feature engineering and data pipelines
- Model development and training
- Model deployment strategies
- Model versioning and lifecycle management
Audience
This course is designed for aspiring data scientists and AI engineers who need to train and manage machine learning models by using Azure Databricks.
Role: AI EngineerData Scientist
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.
Topics
- Introduction1 min
- Get started with Azure Databricks3 min
- Identify Azure Databricks workloads3 min
- Understand key concepts3 min
- Exercise - Explore Azure Databricks30 min
- Knowledge check
- 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
- Knowledge check
- 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
- Knowledge check
- Introduction
- Capabilities of MLflow
- Run experiments with MLflow
- Register and serve models with MLflow
- Exercise - Use MLflow in Azure Databricks
- Knowledge check
- Introduction
- Optimize hyperparameters with Hyperopt
- Review Hyperopt trials
- Scale Hyperopt trials
- Exercise - Optimize hyperparameters for machine learning in Azure Databricks
- Knowledge check
- 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
- Knowledge check
- Introduction
- Understand deep learning concepts
- Train models with PyTorch
- Distribute PyTorch training with Horovod
- Exercise - Train deep learning models on Azure Databricks
- Knowledge check
- Introduction
- Automate your data transformations
- Explore model development
- Explore model deployment strategies
- Explore model versioning and lifecycle management
- Exercise - Manage a machine learning model
- Knowledge check
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
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title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.