Course Description

Course Overview

Implementing a Machine Learning Solution with Microsoft Azure Databricks Training (DP-090) is an intermediate-level training program designed for data scientists and machine learning engineers to gain expertise in building and deploying machine learning solutions using Microsoft Azure Databricks. Participants will learn how to leverage Databricks for data preprocessing, model training, and deployment, and use Azure services for advanced analytics and insights.

Prerequisites

To excel in this course, participants should have a strong understanding of data science concepts, programming languages such as Python or Scala, and machine learning algorithms. Familiarity with cloud computing and Microsoft Azure services is essential. Prior experience with Databricks, Spark, and machine learning frameworks will be highly beneficial.

Methodology

The course will be delivered through a combination of instructor-led lectures, practical demonstrations, hands-on labs, and interactive discussions. Participants will have access to dedicated Azure Databricks environments for hands-on practice, enabling them to apply the concepts learned in real-world scenarios. The course will be led by certified instructors with extensive experience in machine learning and Azure Databricks, ensuring a high-quality learning experience.

Course Outline

• Introduction to Azure Databricks for Machine Learning
• Understanding Databricks as a collaborative workspace
• Overview of Databricks features for data science and machine learning
• Identifying machine learning challenges and solutions with Databricks
• Data Preprocessing and Exploration with Databricks
• Importing and exploring data in Azure Databricks
• Cleaning, transforming, and preparing data for machine learning
• Visualizing and analyzing data with Databricks notebooks
• Building and Training Machine Learning Models with Databricks
• Implementing machine learning algorithms in Databricks
• Training and evaluating models using distributed computing with Spark
• Hyperparameter tuning and model optimization
• Integrating Azure Services for Advanced Analytics
• Using Azure Cognitive Services for text and image analysis
• Implementing Azure Machine Learning service for model management
• Leveraging Azure services for real-time analytics and insights
• Deploying Machine Learning Models with Azure Databricks
• Packaging and deploying models in Databricks
• Creating and managing inference pipelines for real-time predictions
• Monitoring and optimizing deployed models for performance

Outcome

Upon completion of this course, participants will be able to:

• Build and deploy machine learning solutions using Microsoft Azure Databricks.
• Utilize Databricks for data preprocessing, exploration, and visualization.
• Implement machine learning algorithms and train models with Databricks and Spark.
• Integrate Azure services for advanced analytics and real-time insights.
• Deploy and manage machine learning models in Azure Databricks.

Labs

• Importing and Exploring Data in Azure Databricks
• Cleaning and Transforming Data for Machine Learning
• Training and Evaluating Machine Learning Models in Databricks
• Implementing Hyperparameter Tuning and Model Optimization
• Using Azure Cognitive Services for Advanced Analytics
• Deploying Machine Learning Models in Azure Databricks
• Creating Inference Pipelines for Real-Time Predictions