Designing and Implementing a Data Science Solution on Azure
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.
Dla kogo?
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.
Wymagania wstępne
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.
Zakres szkolenia
Module 1: Introduction to Azure Machine Learning
- Getting Started with Azure Machine Learning
- Azure Machine Learning Tools
- Lab: Creating an Azure Machine Learning Workspace
- Lab: Working with Azure Machine Learning Tools
Module 2: No-Code Machine Learning with Designer
- Training Models with Designer
- Publishing Models with Designer
- Lab: Creating a Training Pipeline with the Azure ML Designer
- Lab: Deploying a Service with the Azure ML Designer
Module 3: Running Experiments and Training Models
- Introduction to Experiments
- Training and Registering Models
- Lab: Running Experiments
- Lab: Training and Registering Models
Module 4: Working with Data
- Working with Datastores
- Working with Datasets
- Lab: Working with Datastores
- Lab: Working with Datasets
Module 5: Compute Contexts
- Working with Environments
- Working with Compute Targets
- Lab: Working with Environments
- Lab: Working with Compute Targets
Module 6: Orchestrating Operations with Pipelines
- Introduction to Pipelines
- Publishing and Running Pipelines
- Lab: Creating a Pipeline
- Lab: Publishing a Pipeline
Module 7: Deploying and Consuming Models
- Real-time Inferencing
- Batch Inferencing
- Lab: Creating a Real-time Inferencing Service
- Lab: Creating a Batch Inferencing Service
Module 8: Training Optimal Models
- Hyperparameter Tuning
- Automated Machine Learning
- Lab: Tuning Hyperparameters
- Lab: Using Automated Machine Learning
Module 9: Interpreting Models
- Introduction to Model Interpretation
- Using Model Explainers
- Lab: Reviewing Automated Machine Learning Explanations
- Lab: Interpreting Models
Module 10: Monitoring Models
- Monitoring Models with Application Insights
- Monitoring Data Drift
- Lab: Monitoring a Model with Application Insights
- Lab: Monitoring Data Drift