DP-100: Designing and Implementing a Data Science Solution on Azure
This course teaches you how to operate machine learning solutions at cloud scale using Azure Machine Learning. The course covers how information is prepared for a machine learning model, how the model is trained and published for use, and how to monitor the use of the model in Microsoft Azure.
Implementation: Class, Online
Length: 3 days
Starting dates: Ask for details
Material: Microsoft English Material (MOC)
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.
This course prepares you for the certification exam DP-100: Designing and Implementing a Data Science Solution on Azure to obtain the Microsoft Certified: Azure Data Scientist Associate Certificate.
- Familiar with the basics of Azure and have experience in programming.
- Experience with the Python language as well as the Numpy, Pandas and Matplotlib libraries.
- Understanding the basics of Data Science, i.e., data preparation, storage and how models are trained using public libraries such as Scikit-Learn, PyTorch and Tensorflow.
Module 1: Introduction to Azure Machine Learning
- Getting Started with Azure Machine Learning
- Azure Machine Learning Tools
Module 2: No-Code Machine Learning with Designer
- Training Models with Designer
- Publishing Models with Designer
Module 3: Running Experiments and Training Models
- Introduction to Experiments
- Training and Registering Models
Module 4: Working with Data
- Working with Datastores
- Working with Datasets
Module 5: Compute Contexts
- Working with Environments
- Working with Compute Targe
Module 6: Orchestrating Operations with Pipelines
- Introduction to Pipelines
- Publishing and Running Pipelines
Module 7: Deploying and Consuming Models
- Real-time Inferencing
- Batch Inferencing
Module 8: Training Optimal Models
- Hyperparameter Tuning
- Automated Machine Learning
Module 9: Interpreting Models
- Introduction to Model Interpretation
- Using Model Explainers
Module 10: Monitoring Models
- Monitoring Models with Application Insights
- Monitoring Data Drift