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.
- Set up an Azure Machine Learning workspace (30-35%)
- Run experiments and train models (25-30%)
- Optimize and manage models (20-25%)
- Deploy and consume models (20-25%)
This Certification Exam Prep session is designed for people experienced with data science who are interested in taking the DP-100 exam. Attendees of this session can expect to...
Learn how to use Azure Machine Learning to create and publish machine learning models without writing code.
Roles: AI Engineer, Data Scientist
Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. Learn how to use the Azure Machine Learning Python SDK to create enterprise-ready machine learning solutions.
Roles: Data Scientist, Student
- Introduction to the Azure Machine Learning SDK
- Train a machine learning model with Azure Machine Learning
- Work with Data in Azure Machine Learning
- Work with Compute in Azure Machine Learning
- Orchestrate machine learning with pipelines
- Deploy real-time machine learning services with Azure Machine Learning
- Deploy batch inference pipelines with Azure Machine Learning
- Tune hyperparameters with Azure Machine Learning
- Automate machine learning model selection with Azure Machine Learning
- Explain machine learning models with Azure Machine Learning
- Detect and mitigate unfairness in models with Azure Machine Learning
- Monitor models with Azure Machine Learning
- Monitor data drift with Azure Machine Learning
Machine learning is the foundation for predictive modeling and artificial intelligence. Learn some of the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models.
Roles: Data Scientist