DP-100 Microsoft Azure Data Scientist Associate

Manage data ingestion and preparation.

Free

DP-100 Microsoft Azure Data Scientist Associate

3 days
All levels
0 lessons
0 quizzes
0 students

Course Info

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.

What Will I Learn From This Course?

manage data ingestion and preparation

model training and deployment

machine learning solution monitoring in Microsoft Azure

Pre-requisite

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

Methodology

Lectures, visual presentations, hands-on demo files and lab exercises, Q&A.

Target 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.

Course Outline for This Programme

• Introduction to Azure Machine Learning
• Working with Azure Machine Learning
• Lab : Create an Azure Machine Learning Workspace

• Automated Machine Learning
• Azure Machine Learning Designer
• Lab: Use Automated Machine Learning
• Lab: Use Azure Machine Learning Designer

• Introduction to Experiments
• Training and Registering Models
• Lab: Run Experiments
• Lab: Train Models

• Working with Datastores
• Working with Datasets
• Lab: Work with Data

• Working with Environments
• Working with Compute Targets
• Lab: Work with Compute

• Introduction to Pipelines
• Publishing and Running Pipelines
• Lab: Create a Pipeline

• Real-time Inferencing
• Batch Inferencing
• Continuous Integration and Delivery
• Lab: Create a Real-time Inferencing Service
• Lab: Create a Batch Inferencing Service

• Hyperparameter Tuning
• Automated Machine Learning
• Lab: Tune Hyperparameters
• Lab: Use Automated Machine Learning from the SDK

• Differential Privacy
• Model Interpretability
• Fairness
• Lab: Explore Differential provacy
• Lab: Interpret Models
• Lab: Detect and Mitigate Unfairness

• Monitoring Models with Application Insights
• Monitoring Data Drift
• Lab: Monitor a Model with Application Insights
• Lab: Monitor Data Drift

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