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DP-100 - 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 in Microsoft Azure.

Duration: 4.0 days

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Schedule

Kuala Lumpur | 03 Jul 2023 - 05 Jul 2023 Malaysia
Virtual ILT | 03 Jul 2023 - 05 Jul 2023 Malaysia
Singapore | 05 Jun 2023 - 07 Jun 2023 Singapore
Virtual ILT | 05 Jun 2023 - 07 Jun 2023 Singapore
Virtual ILT | 05 Jun 2023 - 07 Jun 2023 Australia
Virtual ILT | 07 Jun 2023 - 09 Jun 2023 India
Virtual ILT | 15 Aug 2023 - 17 Aug 2023 Thailand
Virtual ILT | 26 Jun 2023 - 29 Jun 2023 Sri Lanka
Virtual ILT | 26 Jun 2023 - 29 Jun 2023 United Arab Emirates

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Objectives

  • Identify your data source and format
  • Choose how to serve data to machine learning workflows
  • Design a data ingestion solution
  • Identify machine learning tasks
  • Choose a service to train a model
  • Choose between computing options
  • Understand how a model will be consumed.
  • Decide whether to deploy your model to a real-time or batch endpoint.
  • Create an Azure Machine Learning workspace.
  • Identify resources and assets.
  • Train models in the workspace.
  • The Azure Machine Learning studio.
  • The Python Software Development Kit (SDK).
  • The Azure Command Line Interface (CLI).
  • Work with Uniform Resource Identifiers (URIs).
  • Create and use datastores.
  • Create and use data assets.
  • Choose the appropriate compute target.
  • Create and use a compute instance.
  • Create and use a compute cluster.
  • Understand environments in Azure Machine Learning.
  • Explore and use curated environments.
  • Create and use custom environments.
  • Prepare your data to use AutoML for classification.
  • Configure and run an AutoML experiment.
  • Evaluate and compare models.
  • Configure to use MLflow in notebooks
  • Use MLflow for model tracking in notebooks
  • Convert a notebook to a script.
  • Test scripts in a terminal.
  • Run a script as a command job.
  • Use parameters in a command job.
  • Use MLflow when you run a script as a job.
  • Review metrics, parameters, artifacts, and models from a run.
  • Create components.
  • Build an Azure Machine Learning pipeline.
  • Run an Azure Machine Learning pipeline.
  • Define a hyperparameter search space.
  • Configure hyperparameter sampling.
  • Select an early-termination policy.
  • Run a sweep job.
  • Use managed online endpoints.
  • Deploy your MLflow model to a managed online endpoint.
  • Deploy a custom model to a managed online endpoint.
  • Test online endpoints.
  • Create a batch endpoint.
  • Deploy your MLflow model to a batch endpoint.
  • Deploy a custom model to a batch endpoint.
  • Invoke batch endpoints.

Content

1. Design a data ingestion strategy for machine learning projects

Learn how to design a data ingestion solution for training data used in machine learning projects.

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2. Design a machine learning model training solution

Learn how to design a model training solution for machine learning projects.

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3. Design a model deployment solution

Learn how to design a model deployment solution and how the requirements of the deployed model can affect the way you train a model.

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4. Explore Azure Machine Learning workspace resources and assets

As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is, and get familiar with all its resources and assets.

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5. Explore developer tools for workspace interaction

Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2).

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6. Make data available in Azure Machine Learning

Learn about how to connect to data from the Azure Machine Learning workspace. You'll be introduced to datastores and data assets.

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7. Work with compute targets in Azure Machine Learning

Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.

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8. Work with environments in Azure Machine Learning

Learn how to use environments in Azure Machine Learning to run scripts on any compute target.

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9. Find the best classification model with Automated Machine Learning

Learn how to find the best classification model with automated machine learning (AutoML). You'll use the Python SDK (v2) to configure and run an AutoML job.

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10. Track model training in Jupyter notebooks with MLflow

Learn how to use MLflow for model tracking when experimenting in notebooks.

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11. Run a training script as a command job in Azure Machine Learning

Learn how to convert your code to a script and run it as a command job in Azure Machine Learning.

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12. Track model training with MLflow in jobs

Learn how to track model training with MLflow in jobs when running scripts.

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13. Run pipelines in Azure Machine Learning

Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.

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14. Perform hyperparameter tuning with Azure Machine Learning

Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.

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15. Deploy a model to a managed online endpoint

Learn how to deploy models to a managed online endpoint for real-time inferencing.

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16. Deploy a model to a batch endpoint

Learn how to deploy models to a batch endpoint. When you invoke a batch endpoint, you'll trigger a batch scoring job.

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Audience

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.

Certification

product-certification

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%)

Course Benefits

product-benefits
  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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