Certified and Advanced Degree
Computer Instructors and Consultants
Course Catalog
Designing and Implementing a Data Science Solution on Azure (DP-100T01)
Code: DP-100
Duration: 4 Day
$2595 USD

OVERVIEW

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 with Azure Machine Learning and MLflow.

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DELIVERY FORMAT

This course is available in the following formats:

Virtual Classroom

Duration: 4 Day
Classroom

Duration: 4 Day

CLASS SCHEDULE
Call 800-798-3901 to enroll in this class!

OUTLINE

Module 1: Explore and configure the Azure Machine Learning workspace

  • Explore Azure Machine Learning workspace resources and assets
  • Explore developer tools for workspace interaction
  • Make data available in Azure Machine Learning
  • Work with compute targets in Azure Machine Learning
  • Work with environments in Azure Machine Learning

Module 2: Experiment with Azure Machine Learning

Module 3: Optimize model training with Azure Machine Learning

  • Run a training script as a command job in Azure Machine Learning
  • Track model training with MLflow in jobs
  • Perform hyperparameter tuning with Azure Machine Learning
  • Run pipelines in Azure Machine Learning

Module 4: Manage and review models in Azure Machine Learning

  • Register an MLflow model in Azure Machine Learning
  • Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

Module 5: Deploy and consume models with Azure Machine Learning

  • Deploy a model to a managed online endpoint
  • Deploy a model to a batch endpoint

Module 6: Develop generative AI apps in Azure AI Foundry portal

  • Introduction to Azure AI Foundry
  • Explore and deploy models from the model catalog in Azure AI Foundry portal
  • Get started with prompt flow to develop language model apps in the Azure AI Foundry
  • Build a RAG-based agent with your own data using Azure AI Foundry
  • Fine-tune a language model with Azure AI Foundry
  • Evaluate the performance of generative AI apps with Azure AI Foundry
  • Responsible generative AI

Module 1: Explore and configure the Azure Machine Learning workspace

  • Explore Azure Machine Learning workspace resources and assets
  • Explore developer tools for workspace interaction
  • Make data available in Azure Machine Learning
  • Work with compute targets in Azure Machine Learning
  • Work with environments in Azure Machine Learning

Module 2: Experiment with Azure Machine Learning

Module 3: Optimize model training with Azure Machine Learning

  • Run a training script as a command job in Azure Machine Learning
  • Track model training with MLflow in jobs
  • Perform hyperparameter tuning with Azure Machine Learning
  • Run pipelines in Azure Machine Learning

Module 4: Manage and review models in Azure Machine Learning

  • Register an MLflow model in Azure Machine Learning
  • Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

Module 5: Deploy and consume models with Azure Machine Learning

  • Deploy a model to a managed online endpoint
  • Deploy a model to a batch endpoint

Module 6: Develop generative AI apps in Azure AI Foundry portal

  • Introduction to Azure AI Foundry
  • Explore and deploy models from the model catalog in Azure AI Foundry portal
  • Get started with prompt flow to develop language model apps in the Azure AI Foundry
  • Build a RAG-based agent with your own data using Azure AI Foundry
  • Fine-tune a language model with Azure AI Foundry
  • Evaluate the performance of generative AI apps with Azure AI Foundry
  • Responsible generative AI
LABS

  • Lab : Creating an Azure Machine Learning Workspace
  • Lab : Use Automated Machine Learning
  • Lab : Use Azure Machine Learning Designer
  • Lab : Run Experiments
  • Lab : Train Models
  • Lab : Work with Data
  • Lab : Work with Compute
  • Lab : Create a Pipeline
  • Lab : Create a Real-time Inferencing Service
  • Lab : Create a Batch Inferencing Service
  • Lab : Tune Hyperparameters
  • Lab : Use Automated Machine Learning from the SDK
  • Lab : Explore Differential privacy
  • Lab : Interpret Models
  • Lab : Detect and Mitigate Unfairness
  • Lab : Monitor a Model with Application Insights
  • Lab : Monitor Data Drift
  • Lab : Creating an Azure Machine Learning Workspace
  • Lab : Use Automated Machine Learning
  • Lab : Use Azure Machine Learning Designer
  • Lab : Run Experiments
  • Lab : Train Models
  • Lab : Work with Data
  • Lab : Work with Compute
  • Lab : Create a Pipeline
  • Lab : Create a Real-time Inferencing Service
  • Lab : Create a Batch Inferencing Service
  • Lab : Tune Hyperparameters
  • Lab : Use Automated Machine Learning from the SDK
  • Lab : Explore Differential privacy
  • Lab : Interpret Models
  • Lab : Detect and Mitigate Unfairness
  • Lab : Monitor a Model with Application Insights
  • Lab : Monitor Data Drift
WHO SHOULD ATTEND

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.

PREREQUISITES

None