MECER MS-DP100T01 Designing and Implement Data Science Solution On Azure User Guide
- June 3, 2024
- MECER
Table of Contents
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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