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

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.

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 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.

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. Introduction to Azure Machine Learning
    • Getting Started with Azure Machine Learning
    • Azure Machine Learning Tools
  2. No-Code Machine Learning with Designer
    • Training Models with Designer
    • Publishing Models with Designer
  3. Running Experiments and Training Models
    • Introduction to Experiments
    • Training and Registering Models
  4. Working with Data
    • Working with Datastores
    • Working with Datasets
  5. Compute Contexts
    • Working with Environments
    • Working with Compute Targets
  6. Orchestrating Operations with Pipelines
    • Introduction to Pipelines
    • Publishing and Running Pipelines
  7. Deploying and Consuming Models
    • Real-time Inferencing
    • Batch Inferencing
  8. Training Optimal Models
    • Hyperparameter Tuning
    • Automated Machine Learning
  9. Interpreting Models
    • Introduction to Model Interpretation
    • Using Model Explainers
  10. Monitoring Models
    • Monitoring Models with Application Insights
    • Monitoring Data Drift

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Length, days (hours)

3 (24)

Closest dates

on request

Price, UAH