Azure Machine Learning Service is Microsoft’s latest offering for developers and data scientists in the custom cloud machine learning and deep learning category. Azure Machine Learning Service adds to a suite of Azure AI products that includes numerous AI toolkits, chatbot and IoT edge services, data science VMs, and pre-built services for vision, speech, language, knowledge, and search.
The AI toolkits include Visual Studio Code Tools for AI, the older drag-and-drop Azure Machine Learning Studio, MMLSpark deep learning tools for Apache Spark, and the Microsoft Cognitive Toolkit, previously known as CNTK, which is being de-emphasized in favor of other machine learning and deep learning frameworks.
Using cloud resources for training deep learning models makes eminent sense in many cases. Using the cloud for training doesn’t necessarily replace the convenience and low operating cost of using your own computer for model building, especially if you have one with lots of RAM and a capable GPU such as an Nvidia Titan RTX. On the other hand, using the cloud offers the opportunity to add compute resources as needed, potentially reducing the time it takes to complete your experiments and find a sufficiently accurate predictive model.
All of the major cloud services now offer machine learning and deep learning development environments. On AWS, that’s primarily Amazon SageMaker, which I reviewed in May 2018. On the Google Cloud Platform, that’s primarily Cloud Machine Learning Engine and the beta Cloud AutoML. On the IBM cloud, that’s primarily IBM Watson Studio. I’ll compare Azure Machine Learning Services with Amazon SageMaker later in this review.