Get started with Machine Learning

In this lab, you'll learn how to use Machine Learning APIs and services in the intelligent cloud and on the intelligent edge.

Intelligent cloud: Machine Learning as a service

As one of the many offerings under Microsoft Azure's paradigm of PaaS (Platform as a Service), Machine Learning as a Service (MLaaS) is an array of services that provide machine learning tools in the cloud. MLaaS mitigates infrastructural concerns such as data pre-processing, model training, model evaluation, and ultimately, predictions. MLaaS clients can take advantage of machine learning without the cost, time, and risk of establishing an in-house machine learning team.

In this lab, you'll try out Azure Cognitive Services, an example of MLaaS. Cognitive Services simplifies the process of building, deploying, and improving classifiers. You'll learn how to use Cognitive Services' REST APIs and Custom Vision's web interface for training classifiers.

Intelligent edge: Windows Machine Learning

Windows ML provides APIs for intelligence on the edge. The platform provides hardware-accelerated evaluation of machine learning models on Windows 10 devices, allowing developers to use machine learning in Windows applications. By leveraging the device's GPU or CPU, Windows ML enables high-performance evaluation of both classical ML and Deep Learning algorithms. Local, on-device evaluation removes concerns of bandwith, connectivity, and privacy, and enables low latency and high performance for quick evaluation results on the edge.

In this lab, you'll learn how to use Windows ML APIs to evaluate various machine learning models trained in Azure's MLaaS.

Prerequisites