DP-100T01 Designing and Implementing a Data Science Solution on Azure
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.
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.
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.
- 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
- AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.
- Create and explore an Azure ML workspace
- Identify resources and assets
- Explore Studio, Python SDK, and CLI
- Create datastores and data assets
- Choose and configure compute instances and clusters
- Use curated and custom environments
- Create components and run pipeline jobs
- Train models in the workspace
- Track training in Jupyter with MLflow
- Convert notebooks to scripts and run jobs
- Perform hyperparameter tuning with sweep jobs
- Evaluate and compare models
- Preprocess data and configure featurization
- Run AutoML experiments
- Select the best classification model
- Register MLflow models
- Deploy to managed online endpoints
- Deploy to batch endpoints and troubleshoot
- Create and explore the Responsible AI dashboard
- Evaluate fairness, transparency, and accountability
- What is Azure AI Foundry?
- Explore model catalog and deploy language models
- Develop LLM apps with Prompt Flow
- Connect data sources and build RAG-based agents
- Prepare and fine-tune language models
- Assess performance of generative AI applications
- Plan responsible AI solutions
- Identify, measure, and mitigate potential harms
- Operate responsible AI systems