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Microsoft Data & Power BIDP-100T01 Designing and Implementing a Data Science Solution on Azure
Run data science and machine learning on Azure.
4 daysIntermediate22 modulesDP-100T01Microsoft Certified: Azure Data Scientist Associate
About this course
Run data science and machine learning on Azure. Over four days you will use the Azure Machine Learning workspace and developer tools, train models with AutoML and command jobs, track them with MLflow, tune hyperparameters, build pipelines, review the Responsible AI dashboard and deploy to managed online endpoints, preparing for DP-100.
Curriculum
22 modules
- Create an Azure Machine Learning workspace
- Identify Azure Machine Learning resources
- Identify Azure Machine Learning assets
- Train models in the workspace
- Explore the studio
- Explore the Python SDK
- Explore the CLI
- Understand URIs
- Create a datastore
- Create a data asset
- Choose the appropriate compute target
- Create and use a compute instance
- Create and use a compute cluster
- Understand environments
- Explore and use curated environments
- Create and use custom environments
- Preprocess data and configure featurization
- Run an Automated Machine Learning experiment
- Evaluate and compare models
- Configure MLflow for model tracking in notebooks
- Train and track models in notebooks
- Convert a notebook to a script
- Run a script as a command job
- Use parameters in a command job
- Track metrics with MLflow
- View metrics and evaluate models
- Define a search space
- Configure a sampling method
- Configure early termination
- Use a sweep job for hyperparameter tuning
- Create components
- Create a pipeline
- Run a pipeline job
- Log models with MLflow
- Understand the MLflow model format
- Register an MLflow model
- Understand Responsible AI
- Create the Responsible AI dashboard
- Evaluate the Responsible AI dashboard
- Explore managed online endpoints
- Deploy your MLflow model to a managed online endpoint
- Deploy a model to a managed online endpoint
- Test managed online endpoints
- Understand and create batch endpoints
- Deploy your MLflow model to a batch endpoint
- Deploy a custom model to a batch endpoint
- Invoke and troubleshoot batch endpoints
- What is Azure AI Foundry?
- How does Azure AI Foundry work
- When to use Azure AI Foundry
- Explore the language models in the model catalog
- Deploy a model to an endpoint
- Improve the performance of a language model
- Understand the development lifecycle of a large language model (LLM) app
- Understand core components and explore flow types
- Explore connections and runtimes
- Explore variants and monitoring options
- Understand how to ground your language model
- Make your data searchable
- Build an agent with prompt flow
- Understand when to fine-tune a language model
- Prepare your data to fine-tune a chat completion model
- Explore fine-tuning language models in Azure AI Studio
- Assess the model performance
- Manually evaluate the performance of a model
- Assess the performance of your generative AI apps
- Plan a responsible generative AI solution
- Identify potential harms
- Measure potential harms
- Mitigate potential harms
- Operate a responsible generative AI solution
Who it's for
Data scientists who use Python and want to train and deploy models on Azure.
Before you start
- 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.
Prepares for
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