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DP-100T01 Designing and Implementing a Data Science Solution on Azure

 

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.

 

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