DP100T01: Designing and Implementing a Data Science Solution on Azure

Designing and Implementing a Data Science Solution on Azure

The Data Scientist is the central role in developing machine learning models. This role is responsible for solving the business problem that initiated the project. While the Data Engineer will prepare the data to be used for the models, the Data Scientist determines what data is needed for model training, creates model features from the data, determines what machine learning model to use, trains and evaluates the model, and often is involved in model deployment. Often the data scientist needs to evaluate multiple models to determine which performs the best.

Duration: 4 Days

Prerequisites

• Understands data science including how to prepare data, train models, and evaluate competing models to select the best one
• How to program in the Python programming language and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn
• Azure Fundamentals course

Audience

• Data Scientists and other individuals with significant responsibilities in training and deploying machine learning models

Learning Objectives

• Learn Azure data science options
• Discuss Azure notebooks
• Register and deploy ML models with AML service
• Automate machine learning model selection
• Automate hyperparameter tuning with HyperDrive
• Manage and monitor machine learning models

Topics

• Doing Data Science on Azure
• Doing Data Science with Azure Machine Learning Service
• Automate Machine Learning with Azure Machine Learning Service
• Manage and Monitor Machine Learning Models with the Azure Machine Learning Service