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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