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

Seminarinformationen

Seminar - Ziel

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Teilnehmer - Zielgruppe

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Kurs - Voraussetzungen

Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.

Specifically:

• Creating cloud resources in Microsoft Azure.
• Using Python to explore and visualize data.
• Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
• Working with containersTo gain these prerequisite skills, take the following free online training before attending the course:
• Explore Microsoft cloud concepts.
• Create machine learning models.
• Administer containers in AzureIf you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
  
  

Seminardauer

• 3 Tage
• 09:00 Uhr bis 17:00 Uhr

Schulungsunterlagen

• nach Absprache

Seminar-Inhalt / Agenda

Introduction to the Azure Machine Learning SDK

Azure Machine Learning provides a cloud-based platform for training, deploying, and managing machine learning models.

In this module, you will learn how to:

• Provision an Azure Machine Learning workspace.
• Use tools and interfaces to work with Azure Machine Learning.
• Run code-based experiments in an Azure Machine Learning workspace.

Use Automated Machine Learning in Azure Machine Learning

Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier.

• Learn how to use the automated machine learning user interface in Azure Machine Learning

Create a classification model with Azure Machine Learning designer

Classification is a supervised machine learning technique used to predict categories or classes. Learn how to create classification models using Azure Machine Learning designer.

• Train and publish a classification model with Azure Machine Learning designer

Train a machine learning model with Azure Machine Learning

Learn how to use Azure Machine Learning to train a model and register it in a workspace.

In this module, you will learn how to:

• Use a ScriptRunConfig to run a model training script as an Azure Machine Learning experiment.
• Create reusable, parameterized training scripts.
• Register trained models.

Work with Data in Azure Machine Learning

Data is the foundation of machine learning. In this module, you will learn how to work with datastores and datasets in Azure Machine Learning, enabling you to build scalable, cloud-based model training solutions.

In this module, you will learn how to:

• Create and use datastores in an Azure Machine Learning workspace.
• Create and use datasets in an Azure Machine Learning workspace.

Work with Compute in Azure Machine Learning

One of the key benefits of the cloud is the ability to use scalable, on-demand compute resources for cost-effective processing of large data. In this module, you'll learn how to use cloud compute in Azure Machine Learning to run training experiments at scale.

In this module, you will learn how to:

• Work with environments
• Work with compute targets

Orchestrate machine learning with pipelines

Orchestrating machine learning training with pipelines is a key element of DevOps for machine learning. In this module, you'll learn how to create, publish, and run pipelines to train models in Azure Machine Learning.

In this module, you will learn how to:

• Create Pipeline steps
• Pass data between steps
• Publish and run a pipeline
• Schedule a pipeline

Deploy real-time machine learning services with Azure Machine Learning

Learn how to register and deploy ML models with the Azure Machine Learning service.

In this module, you will learn how to:

• Deploy a model as a real-time inferencing service.
• Consume a real-time inferencing service.
• Troubleshoot service deployment

Deploy batch inference pipelines with Azure Machine Learning

Machine learning models are often used to generate predictions from large numbers of observations in a batch process. To accomplish this, you can use Azure Machine Learning to publish a batch inference pipeline.

• Learn how to create, publish, and use batch inference pipelines with Azure Machine Learning.

Tune hyperparameters with Azure Machine Learning

Choosing optimal hyperparameter values for model training can be difficult, and usually involved a great deal of trial and error. With Azure Machine Learning, you can leverage cloud-scale experiments to tune hyperparameters.

• Learn how to use Azure Machine Learning hyperparameter tuning experiments to optimize model performance.

Automate machine learning model selection with Azure Machine Learning

Learn how to use automated machine learning in Azure Machine Learning to find the best model for your data.

In this module, you will learn how to:

• Use Azure Machine Learning's automated machine learning capabilities to determine the best performing algorithm for your data.
• Use automated machine learning to preprocess data for training.
• Run an automated machine learning experiment.

Explore differential privacy

Data scientists have an ethical (and often legal) responsibility to protect sensitive data. Differential privacy is a leading edge approach that enables useful analysis while protecting individually identifiable data values.

After completing this module, you'll be able to:

• Articulate the problem of data privacy
• Describe how differential privacy works
• Configure parameters for differential privacy
• Perform differentially private data analysis

Explain machine learning models with Azure Machine Learning

Many decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions models make.

• Learn how to explain models by calculating and interpreting feature importance.

Detect and mitigate unfairness in models with Azure Machine Learning

Machine learning models can often encapsulate unintentional bias that results in unfairness. With Fairlearn and Azure Machine Learning, you can detect and mitigate unfairness in your models.

In this module, you will learn:

• How to evaluate machine learning models for fairness.
• How to mitigate predictive disparity in a machine learning model.

Monitor data drift with Azure Machine Learning

Changing trends in data over time can reduce the accuracy of the predictions made by a model. Monitoring for this data drift is an important way to ensure your model continues to predict accurately.

• Learn how to monitor data drift in Azure Machine Learning.

Monitor models with Azure Machine Learning

After a machine learning model has been deployed into production, it's important to understand how it is being used by capturing and viewing telemetry.

• Learn how to use Azure Application Insights to monitor a deployed Azure Machine Learning model.

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