Programming & Development

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


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.

Who Should Attend
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 Objectives
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.

Course Outline:

Introduction to Azure Machine Learning
Working with Azure Machine Learning

Automated Machine Learning
Azure Machine Learning Designer

Introduction to Experiments
Training and Registering Models

Working with Datastores
Working with Datasets

Working with Environments
Working with Compute Targets

Introduction to Pipelines
Publishing and Running Pipelines

Real-time Inferencing
Batch Inferencing
Continuous Integration and Delivery

Hyperparameter Tuning
Automated Machine Learning

Differential Privacy
Model Interpretability

Monitoring Models with Application Insights
Monitoring Data Drift

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