Course DP-100T01 Designing and Implementing a Data Science Solution on Azure | nt.ua

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Course DP-100T01 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.

The course will be useful for companies that have transferred a large amount of data to Azure, plan to use Data Lake, structure and centrally process data.

After completing this course, students will be able to:

  • use the Python programming language for machine learning in Microsoft Azure;
  • manage the acquisition and preparation of data, training and deployment of models, monitoring of machine learning solutions in the cloud;
  • get experience with Scikit-Learn, PyTorch and Tensorflow.

Audience Profile

This course is designed for Analytics, Data Scientists, Data Engineers with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who know how to program, and want to build and operate machine learning solutions in the cloud.

The task is to analyze data obtained from different sources, transform it, search for dependencies, trends, statistics.

Before attending this course, students must have:

  • A fundamental knowledge of Microsoft Azure.
  • Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
  • Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.
  1. Design a machine learning solution
    • Design a data ingestion strategy for machine learning projects
    • Design a machine learning model training solution
    • Designing a model deployment solution
    • Design a machine learning operations (MLOps)solution
  2. Explore the Azure Machine Learning workspace
    • Explore Azure Machine Learning workspace resources and assets
    • Explore developer tools for workspace interaction
    • Make data available in Azure Machine Learning
    • Work with compute targets in Azure Machine Learning
    • Work with environments in Azure Machine Learning
  3. Experiment with Azure Machine Learning
    • Explore Automated Machine Learning
    • Find the best classification model with Automated Machine Learning
    • Track model training in Jupyter notebooks with MLflow
  4. Optimize model training with Azure Machine Learning
    • Run a training script as a command job in Azure Machine Learning
    • Track model training with MLflow in jobs
    • Perform hyperparameter tuning with Azure Machine Learning
    • Run pipelines in Azure Machine Learning
  5. Manage and evaluate models with Azure Machine Learning
    • Register an MLflow model in Azure Machine Learning
    • Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
  6. Deploy and consume models with Azure Machine Learning
    • Deploy a model to a managed online endpoint
    • Deploy a model to a batch endpoint

Sign up for the closest date

Course Code

DP-100T01

Exam Code

DP-100

Length, days (hours)

4 (32)

Closest dates

on request

Price, UAH