Training Microsoft

Training goals

code: DP-100T01

The Azure Data Scientist applies their knowledge of data science and machine learning to implementing and running machine learning workloads on Microsoft Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.

 

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.

Conspect Show list

  1. Introduction to the Azure Machine Learning SDK
  2. Use Automated Machine Learning in Azure Machine Learning
  3. Create a classification model with Azure Machine Learning designer
  4. Train a machine learning model with Azure Machine Learning
  5. Work with Data in Azure Machine Learning
  6. Work with Compute in Azure Machine Learning
  7. Orchestrate machine learning with pipelines
  8. Deploy real-time machine learning services with Azure Machine Learning
  9. Deploy batch inference pipelines with Azure Machine Learning
  10. Tune hyperparameters with Azure Machine Learning
  11. Automate machine learning model selection with Azure Machine Learning
  12. Explore differential privacy
  13. Explain machine learning models with Azure Machine Learning
  14. Detect and mitigate unfairness in models with Azure Machine Learning
  15. Monitor data drift with Azure Machine Learning
  16. Monitor models with Azure Machine Learning
Download conspect training as PDF

Additional information

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.
Difficulty level
Duration 3 days
Certificate

Certificate of completing an authorized Microsoft training
Trainer

Microsoft Certified Trainer

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    • General information
    • Guaranteed dates
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    • Language of the training
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    • General information
    • Guaranteed dates
    • Last minute (-10%)
    • Language of the training
    • English
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Traditional training

Sessions organised at Compendium CE are usually held in our locations in Kraków and Warsaw, but also in venues designated by the client. The group participating in training meets at a specific place and specific time with a coach and actively participates in laboratory sessions.

Dlearning training

You may participate from at any place in the world. It is sufficient to have a computer (or, actually a tablet, or smartphone) connected to the Internet. Compendium CE provides each Distance Learning training participant with adequate software enabling connection to the Data Center. For more information, please visit dlearning.eu site

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Paper materials

Traditional materials: The price includes standard materials issued in the form of paper books, printed or other, depending on the arrangements with the manufacturer.

Electronic materials

Electronic materials: These are electronic training materials that are available to you based on your specific application: Skillpipe, eVantage, etc., or as PDF documents.

Ctab materials

Ctab materials: the price includes ctab tablet and electronic training materials or traditional training materials and supplies provided electronically according to manufacturer's specifications (in PDF or EPUB form). The materials provided are adapted for display on ctab tablets. For more information, check out the ctab website.

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