- Course overview
- Course details
- Prerequisites
Course overview
About this course
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.
Audience profile
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.
Course details
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
Prerequisites
- 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.
Enquiry
Course : Designing and Implementing a Data Science Solution on Azure
Enquiry
request for : Designing and Implementing a Data Science Solution on Azure
