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

Course Information

Course Name

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

Exam code

DP-100

Duration

4 Days

Certification

Microsoft Certified: Azure Data Scientist Associate

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.

Audience Profile

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

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.

At Course Completion

Course Outline

Module 1: Explore Azure Machine Learning workspace resources and assets

Module 2: Explore developer tools for workspace interaction

Module 3: Make data available in Azure Machine Learning

Module 4: Work with compute targets in Azure Machine Learning

Module 5: Work with environments in Azure Machine Learning

Module 6: Find the best classification model with Automated Machine Learning

Module 7: Track model training in Jupyter notebooks with MLflow

Module 8: Run a training script as a command job in Azure Machine Learning

Module 9: Track model training with MLflow in jobs

Module 10: Perform hyperparameter tuning with Azure Machine Learning

Module 11: Run pipelines in Azure Machine Learning

Module 12: Register an MLflow model in Azure Machine Learning

Module 13: Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

Module 14: Deploy a model to a managed online endpoint

Module 15: Deploy a model to a batch endpoint

Module 16: Plan and prepare to develop AI solutions on Azure

Module 17: Explore and deploy models from the model catalog in Azure AI Foundry portal

Module 18: Develop an AI app with the Azure AI Foundry SDK

Module 19: Get started with prompt flow to develop language model apps in the Azure AI Foundry

Module 20: Build a RAG-based agent with your own data using Azure AI Foundry

Module 21: Fine-tune a language model with Azure AI Foundry

Module 22: Evaluate the performance of generative AI apps with Azure AI Foundry

Module 23: Responsible generative AI

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