Build and Operate Machine Learning Solutions with Azure

Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions. This is the third course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization 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. Each course teaches you the concepts and skills that are measured by the exam. This Specialization is intended 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. It teaches data scientists how to create end-to-end solutions in Microsoft Azure. Students will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions, and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.

• Use the Azure Machine Learning SDK to train a model
• Azure Machine Learning provides a cloud-based platform for training, deploying, and managing machine learning models. In this module, you will learn how to provision an Azure Machine Learning workspace. You will use tools and interfaces to work with Azure Machine Learning and run code-based experiments in an Azure Machine Learning workspace. finally, you will learn how to use Azure Machine Learning to train a model and register it in a workspace.
• Work with Data and Compute in Azure Machine Learning
• Data is the foundation of machine learning. In this module, you will learn how to work with datastores and datasets in Azure Machine Learning, enabling you to build scalable, cloud-based model training solutions. You'll also learn how to use cloud compute in Azure Machine Learning to run training experiments at scale.
• Orchestrate pipelines and deploy real-time machine learning services with Azure Machine Learning
• Orchestrating machine learning training with pipelines is a key element of DevOps for machine learning. In this module, you'll learn how to create, publish, and run pipelines to train models in Azure Machine Learning. You'll also learn how to register and deploy ML models with the Azure Machine Learning service.
• Deploy batch inference pipelines and tune hyperparameters with Azure Machine Learning
• Machine learning models are often used to generate predictions from large numbers of observations in a batch process. You will accomplish this using Azure Machine Learning to publish a batch inference pipeline. You will also leverage cloud-scale experiments to choose optimal hyperparameter values for model training.
• Select models and protect sensitive data
• In this module, you will learn how to use automated machine learning in Azure Machine Learning to find the best model for your data. You will learn how differential privacy is a leading edge approach that enables useful analysis while protecting individually identifiable data values. You will also learn about the factors that influence the predictions models make.
• Monitor machine learning deployments
• Machine learning models can often encapsulate unintentional bias that results in unfairness. In this module, you will learn how to use Fairlearn and Azure Machine Learning to detect and mitigate unfairness in your models. You will learn how to use telemetry to understand how a machine learning model is being used once it has been deployed into production. Finally, you will learn how to monitor data drift to ensure your model continues to predict accurately.