MECER MS-DP100T01 Designing and Implement Data Science Solution On Azure User Guide

MECER MS-DP100T01 Designing and Implement Data Science Solution On Azure

DURATION| LEVEL| TECHNOLOGY| DELIVERY
METHOD| TRAINING
CREDITS
---|---|---|---|---
3 Days| Intermediate| Azure| Instructor-led| NA

INTRODUCTION

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.

AUDIENCE PROFILE

This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.

PREREQUISITES

Before attending this course, students must have:

• Azure Fundamentals
• Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
• How to program in the Python programming language and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.

COURSE OBJECTIVES

After completing this course, students will be able to:

• Understand the data science in Azure
• Use Machine Learning to automate the end-to-end process
• Manage and monitor the Machine Learning service

Module 1: Getting Started with Azure Machine Learning
In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.
Lessons

• Introduction to Azure Machine Learning
• Working with Azure Machine Learning
• Lab: Create an Azure Machine Learning Workspace
• Provision an Azure Machine Learning workspace
• Use tools and code to work with Azure Machine Learning

Module 2: Visual Tools for Machine Learning
This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.
Lessons

• Automated Machine Learning
• Azure Machine Learning Designer

Lab: Use Automated Machine Learning
Lab: Use Azure Machine Learning Designer
After completing this module, you will be able to

• Use automated machine learning to train a machine learning model
• Use Azure Machine Learning designer to train a model

Module 3: Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing, model training code, and use them to train machine learning models. Lessons

• Introduction to Experiments
• Training and Registering Models

Lab: Train Models
Lab: Run Experiments
After completing this module, you will be able to

• Run code-based experiments in an Azure Machine Learning workspace
• Train and register machine learning models

Module 4: Working with Data Data
is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
Lessons

• Working with Datastores
• Working with Datasets

Lab: Work with Data
After completing this module, you will be able to

• Create and use datastores
• Create and use datasets

Module 5: Working with Compute
One of the key benefits of the cloud is the ability to leverage compute resources on demand and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you’ll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
Lessons

• Working with Environments
• Working with Compute Targets

Lab: Work with Compute
After completing this module, you will be able to

• Create and use environments
• Create and use compute targets

Module 6: Orchestrating Operations with Pipelines
Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it’s time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you’ll explore how to define and run them in this module.
Lessons

• Introduction to Pipelines
• Publishing and Running Pipelines

Lab: Create a Pipeline
After completing this module, you will be able to

• Create pipelines to automate machine learning workflows
• Publish and run pipeline services

Module 7: Deploying and Consuming Models
Models are designed to help decision making through predictions, so they’re only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
Lessons

• Real-time Inferencing
• Batch Inferencing
• Continuous Integration and Delivery

Lab: Create a Real-time Inferencing Service
Lab: Create a Batch Inferencing Service
After completing this module, you will be able to

• Publish a model as a real-time inference service
• Publish a model as a batch inference service
• Describe techniques to implement continuous integration and delivery

Module 8: Training Optimal Models
By this stage of the course, you’ve learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you’ll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.
Lessons

• Hyperparameter Tuning
• Automated Machine Learning

Lab: Use Automated Machine Learning from the SDK
Lab: Tune Hyperparameters After completing this module, you will be able to

• Optimize hyperparameters for model training
• Use automated machine learning to find the optimal model for your data

Module 9: Responsible Machine Learning
Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles. Lessons

• Differential Privacy
• Model Interpretability
• Fairness

Lab: Explore Differential provacy
Lab: Interpret Models
Lab: Detect and Mitigate Unfairness After completing this module, you will be able to

• Apply differential provacy to data analysis
• Use explainers to interpret machine learning models
• Evaluate models for fairness

Module 10: Monitoring Models
After a model has been deployed, it’s important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data. Lessons

• Monitoring Models with Application Insights
• Monitoring Data Drift

Lab: Monitor Data Drift
Lab: Monitor a Model with Application Insights
After completing this module, you will be able to

• Use Application Insights to monitor a published model
• Monitor data drift

ASSOCIATED CERTIFICATIONS & EXAM

This course will prepare delegates to write the Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure exam.

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