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DP-100T01 - Designing and implementing a data science solution on Azure

Duration: 4.0 days

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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1. Design a machine learning solution

There are many options on Azure to train and consume machine learning models. Which service best fits your scenario can depend on a myriad of factors. Learn how to identify important requirements and when to use which service when you want to use machine learning models.

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2. Explore and configure the Azure Machine Learning workspace

Throughout this learning path you explore and configure the Azure Machine Learning workspace. Learn how you can create a workspace and what you can do with it. Explore the various developer tools you can use to interact with the workspace. Configure the workspace for machine learning workloads by creating data assets and compute resources.

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3. Experiment with Azure Machine Learning

Learn how to find the best model with automated machine learning (AutoML) and by experimenting in notebooks.

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4. Optimize model training with Azure Machine Learning

Learn how to optimize model training in Azure Machine Learning by using scripts, jobs, components and pipelines.

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5. Manage and review models in Azure Machine Learning

Learn how to manage and review models in Azure Machine Learning by using MLflow to store your model files and using responsible AI features to evaluate your models.

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6. Deploy and consume models with Azure Machine Learning

Learn how to deploy a model to an endpoint. When you deploy a model, you can get real-time or batch predictions by calling the endpoint.

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DP-100T01 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.

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Skills Measured

  • Design and prepare a machine learning solution (20–25%)
  • Explore data and train models (35–40%)
  • Prepare a model for deployment (20–25%)
  • Deploy and retrain a model (10–15%)




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