DP-100T01 Designing And Implementing A Data Science Solution On Azure
Length: 4 Day(s), Price: $$2,682.00 exl GST
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 with Azure Machine Learning and MLflow.
- Gain proficiency in operating machine learning solutions at cloud scale using Azure Machine Learning.
- Learn to manage data ingestion and preparation for machine learning projects leveraging Python and Azure Machine Learning.
- Develop skills in model training and deployment within the Azure Machine Learning framework.
- Understand the process of monitoring machine learning solutions using Azure Machine Learning and MLflow.
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.
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
- If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
1. Design a data ingestion strategy for machine learning projects
2. Design a machine learning model training solution
3. Design a model deployment solution
4. Explore Azure Machine Learning workspace resources and assets
5. Explore developer tools for workspace interaction
6. Make data available in Azure Machine Learning
7. Work with compute targets in Azure Machine Learning
8. Work with environments in Azure Machine Learning
9. Find the best classification model with Automated Machine Learning
10. Track model training in Jupyter notebooks with MLflow
11. Run a training script as a command job in Azure Machine Learning
12. Track model training with MLflow in jobs
13. Run pipelines in Azure Machine Learning
14. Perform hyperparameter tuning with Azure Machine Learning
15. Deploy a model to a managed online endpoint
16. Deploy a model to a batch endpoint