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

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 containers

To 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 Azure

If 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

Module 1: Getting Started with Azure Machine Learning

  • Introduction to Azure Machine Learning
  • Working with Azure Machine Learning

Module 2: No-Code Machine Learning

  • Automated Machine Learning
  • Azure Machine Learning Designer

Module 3: Running Experiments and Training Models

  • Introduction to Experiments
  • Training and Registering Models

Module 4: Working with Data

  • Working with Datastores
  • Working with Datasets

Module 5: Working with Compute

  • Working with Environments
  • Working with Compute Targets

Module 6: Orchestrating Operations with Pipelines

  • Introduction to Pipelines
  • Publishing and Running Pipelines

Module 7: Deploying and Consuming Models

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

Module 8: Training Optimal Models

  • Hyperparameter Tuning
  • Automated Machine Learning

Module 9: Responsible Machine Learning

  • Differential Privacy
  • Model Interpretability
  • Fairness

Module 10: Monitoring Models

  • Monitoring Models with Application Insights
  • Monitoring Data Drift

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