About This Course

The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on 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.

By completing these labs, users will have good knowledge of cloud concepts, Azure services. User should be familiar with the general technology concepts, including concepts of networking, storage, compute, application support, and application development, Azure ML Workspace and Machine learning.

These hands-on labs are provided with a ready to use Azure environment along with detailed personalized instructions to learn and complete the exercises to prepare for DP-100 certification.

Each module lab environment is available only for the specified duration and can be taken only once within 30 days of enrollment.

Note: For performing the labs, you need to ensure you are clicking on Launch Lab button only when you are ready to perform the lab as you will be able to launch the lab only once.

Modules Included:

Introduction to Azure Machine Learning
Use Automated Machine Learning
AzureML Designer
Run Experiments
Train Models
Work with Data
Work with Compute
Create a Pipeline
Create a Real-time Inference Service
Create a Batch Inferencing Service
Tune Hyperparameters
Use Automated Machine Learning from the SDK
Explore Differential Privacy
Interpret Models
Detect and Mitigate Unfairness
Monitor a Model
Monitor Data Drift

Requirements

This course doesn’t have any technical pre-requisite. This course primarily uses the Azure portal to create services and does not require scripting skills.

Frequently Asked Questions