DP-100T01 Designing and Implementing a Data Science Solution on Azure
Duration: 4 Days
Course Overview
This Designing and Implementing a Data Science Solution on Azure DP-100 Course plays a vital role in transforming raw data into actionable insights, making it more relevant than ever. As organisations harness the power of data to drive decision-making, this Microsoft Azure Certification provides essential knowledge for professionals looking to navigate the dynamic landscape of data science on Microsoft Azure.
Proficiency in Data Science on Microsoft Azure is crucial for professionals in various domains, including Data Analysts, Data Engineers, Machine Learning Engineers, and aspiring Data Scientists. This knowledge empowers them to harness the extensive capabilities of Microsoft Azure, allowing them to make informed decisions, build innovative solutions, and stay competitive in the evolving digital world.
In this 4-day Designing and Implementing a Data Science Solution on Azure DP-100 Training Course, delegates will learn essential skills and knowledge to design and implement data science solutions on Azure. They will gain expertise in data ingestion strategies, model training, model deployment, workspace resources, developer tools environments, and various aspects of Machine Learning and data pipelines.
Course Objectives
To understand the principles of designing Data Science solutions on Azure
To gain proficiency in data ingestion, model training, and model deployment
To learn to utilise Azure Machine Learning workspace resources and tools
To acquire skills to work with compute targets and create data pipelines
To master the art of performing model evaluation and deployment
To develop expertise in tracking and managing models using MLflow
After completing this course, delegates become proficient in designing and implementing data science solutions on Microsoft Azure. They will be armed with comprehensive knowledge and practical skills. Graduates are prepared to tackle real-world data challenges. They can confidently apply their expertise in various domains, enabling businesses to harness the full potential of their data.
Course Outline
Module 1: Design a Data Ingestion Strategy for Machine Learning Projects
Introduction
Identify Your Data Source and Format
Choose How to Serve Data to Machine Learning Workflows
Design a Data Ingestion Solution
Exercise: Design a Data Ingestion Strategy
Module 2: Design a Machine Learning Model Training Solution
Introduction
Identify Machine Learning Tasks
Choose a Service to Train a Machine Learning Model
Decide Between Compute Options
Exercise: Design a Model Training Strategy
Module 3: Design a Model Deployment Solution
Introduction
Understand How Model Will Be Consumed
Decide on Real-Time or Batch Deployment
Exercise - Design a Deployment Solution
Module 4: Azure Machine Learning Workspace Resources and Assets
Introduction
Video - Explore the Azure Machine Learning Workspace
Create an Azure Machine Learning Workspace
Identify Azure Machine Learning Resources
Identify Azure Machine Learning Assets
Train Models in the Workspace
Exercise - Explore the Workspace
Module 5: Developer Tools for Workspace Interaction
Introduction
Studio
Python SDK
CLI
Exercise-Explore the Developer Tools
Module 6: Make Data Available in Azure Machine Learning
Introduction
Video - Make Data Available in Azure Machine Learning
Understand URIs
Create a Datastore
Create a Data Asset
Exercise - Make Data Available in Azure Machine Learning
Module 7: Work with Compute Targets in Azure Machine Learning
Introduction
Choose the Appropriate Compute Target
Create and Use a Compute Instance
Create and Use a Compute Cluster
Exercise - Work with Compute Resources
Module 8: Work with Environments in Azure Machine Learning
Introduction
Understand Environments
Use Curated Environments
Create and Use Custom Environments
Exercise - Work with Environments
Module 9: Classification Model with Automated Machine Learning
Introduction
Video - Find the Best Classification Model with Automated Machine Learning
Preprocess Data and Configure Featurisation
Run an Automated Machine Learning Experiment
Evaluate and Compare Models
Exercise - Find the Best Classification Model with Automated Machine Learning
Module 10: Track Model Training in Jupyter Notebooks with MLflow
Introduction
Configure MLflow For Model Tracking in Notebooks
Train and Track Models in Notebooks
Exercise - Track Model Training
Module 11: Run Training Script as a Command Job in Azure Machine Learning
Introduction
Video - Run a Training Script as a Command Job in Azure Machine Learning
Convert a Notebook to a Script
Run a Script as a Command Job
Use Parameters in a Command Job
Exercise - Run a Training Script as a Command Job
Module 12: Track Model Training with MLflow in Jobs
Introduction
Video - Track Model Training with MLFlow in Jobs
Track Metrics with MLflow
View Metrics and Evaluate Models
Exercise - Use MLflow to Track Training Jobs
Module 13: Run Pipelines in Azure Machine Learning
Introduction
Video - Run Pipelines in Azure Machine Learning
Create Components
Create a Pipeline
Run a Pipeline Job
Exercise - Run a Pipeline Job
Module 14: Perform Hyperparameter Tuning with Azure Machine Learning
Introduction
Define a Search Space
Configure a Sampling Method
Configure Early Termination
Use a Sweep Job for Hyperparameter Tuning
Exercise - Run a Sweep Job
Module 15: Deploy a Model to a Managed Online Endpoint
Introduction
Managed Online Endpoints
Deploy the MLflow Model to a Managed Online Endpoint
Deploy a Model to a Managed Online Endpoint
Test Managed Online Endpoints
Exercise - Deploy an MLflow Model to an Online Endpoint
Module 16: Deploy a Model to a Batch Endpoint
Introduction
Video - Deploy a Model to a Batch Endpoint
Understand and Create Batch Endpoints
Deploy Your MLflow Model to a Batch Endpoint
Deploy a Custom Model to a Batch Endpoint
Invoke and Troubleshoot Batch Endpoints
Exercise - Deploy an MLflow Model to a Batch Endpoint
Who should attend this
This Designing and Implementing a Data Science Solution on Azure (DP-100) Course is designed to introduce professionals to Azure's Data Science and Machine Learning solutions. This Microsoft Azure Certification can be beneficial to a wide range of professionals, including:
Data Scientists
Azure Data Engineers
Data Analysts
AI Engineers
Cloud Solution Architects
Machine Learning Developers
Big Data Engineers
Research Scientists
Prerequisites
For attending this Designing and Implementing a Data Science Solution on Azure DP-100 Course, the required prerequisites are proficiency in one of the programming languages like Python, R, or SQL and a basic knowledge about Machine Learning and Azure Machine Learning.