DP-100 Designing and Implementing a Data Science Solution on Azure
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning.
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 in Microsoft Azure.
Before attending this course, students must have:
- A fundamental knowledge of Microsoft Azure
- Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
- Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.
Who Should Attend?
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.
- Getting Started with Azure Machine Learning
- Azure Machine Learning Tools
Labs: Creating an Azure Machine Learning Workspace
Labs: Working with Azure Machine Learning Tools
- Training Models with Designer
- Publishing Models with Designer
Labs: Creating a Training Pipeline with the Azure ML Designer
Labs: Deploying a Service with the Azure ML Designer
- Introduction to Experiments
- Training and Registering Models
Labs: Running Experiments
Labs: Training and Registering Models
- Working with Datastores
- Working with Datasets
Labs: Working with Datastores
Labs: Working with Datasets
- Working with Environments
- Working with Compute Targets
Labs: Working with Environments
Labs: Working with Compute Targets
- Introduction to Pipelines
- Publishing and Running Pipelines
Labs: Creating a Pipeline
Labs: Publishing a Pipeline
- Real-time Inferencing
- Batch Inferencing
Labs: Creating a Real-time Inferencing Service
Labs: Creating a Batch Inferencing Service
- Hyperparameter Tuning
- Automated Machine Learning
Labs: Tuning Hyperparameters
Labs: Using Automated Machine Learning
- Introduction to Model Interpretation
- Using Model Explainers
Labs: Reviewing Automated Machine Learning Explanations
Labs: Interpreting Models
- Monitoring Models with Application Insights
- Monitoring Data Drift
Labs: Monitoring a Model with Application Insights
Labs: Monitoring Data Drift