Overview

In this course students will gain the necessary knowledge about how to use Azure services to develop, train, and deploy, machine learning solutions. The course starts with an overview of Azure services that support data science. From there, it focuses on using Azure's premier data science service, Azure Machine Learning service, to automate the data science pipeline. This course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.

Pass the DP-100 Designing and Implementing a Data Science Solution on Azure exam to be awarded the Microsoft Certified: Azure Data Scientist Associate certification.
Students learn how to develop data models that solve business problems using Azure technologies.

The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.

This course is 50% presentation and demonstration and 50% hands-on learning using Microsoft Azure.

Benefits Includes 6 Months of Skill Me UP Academy

Course Objectives

• Select development environment
• Set up development environment
• Quantify the business problem
• Transform data into usable datasets
• Perform Exploratory Data Analysis (EDA)
• Cleanse and transform data
• Perform feature extraction
• Perform feature selection
• Select an algorithmic approach
• Split datasets
• Identify data imbalances
• Train the model
• Evaluate model performance

Course Modules

Course Outline

Module 1: Introduction to Azure Machine Learning

• Getting Started with Azure Machine Learning
• Azure Machine Learning Tools

Module 2: "No-code" Machine Learning with Designer

• Training Models with Designer
• Publishing Models with Designer

Module 3: Running Experiments and Training Models

• Introduction to Experiments
• Training and Registering Models

Module 4: Working with Data

• Working with Datastores
• Working with Datasets

Module 5: Compute Contexts

• Working with Environments
• Working with Compute Targets

Module 6: Orchestrating Operations with Pipelines

• Introduction to Pipelines
• Publishing and Running Pipelines

Module 7: Deploying and Consuming Models

• Real-time Inferencing
• Batch Inferencing

Module 8: Training Optimal Models

• Hyperparameter Tuning
• Automated Machine Learning

Module 9: Interpreting Models

• Introduction to Model Interpretability
• Using Model Explainers


Module 10: Monitoring Models

• Monitoring Models with Application Insights
• Monitoring Data Drift



;