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.

INR 45000 + tax

Date : 06 Dec 2022

Code: dp-100

Duration: 3.0 days

other dates

Schedule

Virtual ILT | 06 Dec 2022 - 08 Dec 2022
Virtual ILT | 04 Jan 2023 - 06 Jan 2023
Virtual ILT | 01 Feb 2023 - 03 Feb 2023
Virtual ILT | 01 Mar 2023 - 03 Mar 2023
Virtual ILT | 04 Apr 2023 - 06 Apr 2023
Virtual ILT | 10 May 2023 - 12 May 2023
Virtual ILT | 07 Jun 2023 - 09 Jun 2023

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Objectives

  • Provision an Azure Machine Learning workspace.
  • Use tools and interfaces to work with Azure Machine Learning.
  • Run code-based experiments in an Azure Machine Learning workspace.
  • Train and publish a classification model with Azure Machine Learning designer
  • Use a ScriptRunConfig to run a model training script as an Azure Machine Learning experiment.
  • Create reusable, parameterized training scripts.
  • Register trained models.
  • Create and use datastores in an Azure Machine Learning workspace.
  • Create and use datasets in an Azure Machine Learning workspace.
  • Work with environments
  • Work with compute targets
  • Create Pipeline steps
  • Pass data between steps
  • Publish and run a pipeline
  • Schedule a pipeline
  • Deploy a model as a real-time inferencing service.
  • Consume a real-time inferencing service.
  • Troubleshoot service deployment
  • Use Azure Machine Learning's automated machine learning capabilities to determine the best performing algorithm for your data.
  • Use automated machine learning to preprocess data for training.
  • Run an automated machine learning experiment.
  • Articulate the problem of data privacy
  • Describe how differential privacy works
  • Configure parameters for differential privacy
  • Perform differentially private data analysis
  • How to evaluate machine learning models for fairness.
  • How to mitigate predictive disparity in a machine learning model.

Content

1. Introduction to the Azure Machine Learning SDK

Azure Machine Learning provides a cloud-based platform for training, deploying, and managing machine learning models.

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2. Use Automated Machine Learning in Azure Machine Learning

Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier.

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3. Create a classification model with Azure Machine Learning designer

Classification is a supervised machine learning technique used to predict categories or classes. Learn how to create classification models using Azure Machine Learning designer.

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4. Train a machine learning model with Azure Machine Learning

Learn how to use Azure Machine Learning to train a model and register it in a workspace.

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5. Work with Data in Azure Machine Learning

Data is the foundation of machine learning. In this module, you will learn how to work with datastores and datasets in Azure Machine Learning, enabling you to build scalable, cloud-based model training solutions.

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6. Work with Compute in Azure Machine Learning

One of the key benefits of the cloud is the ability to use scalable, on-demand compute resources for cost-effective processing of large data. In this module, you'll learn how to use cloud compute in Azure Machine Learning to run training experiments at scale.

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7. Orchestrate machine learning with pipelines

Orchestrating machine learning training with pipelines is a key element of DevOps for machine learning. In this module, you'll learn how to create, publish, and run pipelines to train models in Azure Machine Learning.

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8. Deploy real-time machine learning services with Azure Machine Learning

Learn how to register and deploy ML models with the Azure Machine Learning service.

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9. Deploy batch inference pipelines with Azure Machine Learning

Machine learning models are often used to generate predictions from large numbers of observations in a batch process. To accomplish this, you can use Azure Machine Learning to publish a batch inference pipeline.

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10. Tune hyperparameters with Azure Machine Learning

Choosing optimal hyperparameter values for model training can be difficult, and usually involved a great deal of trial and error. With Azure Machine Learning, you can leverage cloud-scale experiments to tune hyperparameters.

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11. Automate machine learning model selection with Azure Machine Learning

Learn how to use automated machine learning in Azure Machine Learning to find the best model for your data.

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12. Explore differential privacy

Data scientists have an ethical (and often legal) responsibility to protect sensitive data. Differential privacy is a leading edge approach that enables useful analysis while protecting individually identifiable data values.

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13. Explain machine learning models with Azure Machine Learning

Many decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions models make.

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14. Detect and mitigate unfairness in models with Azure Machine Learning

Machine learning models can often encapsulate unintentional bias that results in unfairness. With Fairlearn and Azure Machine Learning, you can detect and mitigate unfairness in your models.

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15. Monitor data drift with Azure Machine Learning

Changing trends in data over time can reduce the accuracy of the predictions made by a model. Monitoring for this data drift is an important way to ensure your model continues to predict accurately.

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16. Monitor models with Azure Machine Learning

After a machine learning model has been deployed into production, it's important to understand how it is being used by capturing and viewing telemetry.

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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:

If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.

 

Certification

product-certification

Microsoft Certified: Azure Data Scientist Associate

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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