Thomas Wood, freelance data scientist in London, UK. Speciality area: natural language processing (NLP)

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Cloud machine learning services and Microsoft Azure ML

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I am able to work with any cloud AI environment to develop and deploy machine learning models to production.

Traditionally, if you wanted to train and deploy a machine learning model online, for example a price prediction model on a website, you would have had to rent or buy a server and invest a lot of time maintaining it. This was both expensive and troublesome.

Cloud AI, or cloud machine learning, means the practice of renting somebody else’s computers (‘the cloud’) to train and deploy machine learning models. You pay for the usage of the cloud provider’s infrastructure instead of making large upfront investments, and the computations take place in the cloud. Examples include Google AI, Amazon AWS and Microsoft Azure.

There are several advantages of using a cloud AI environment to build your models:

For this reason many organisations, including most UK public sector bodies, prefer to work with cloud providers such as Microsoft, Google and Amazon when they commission AI models.

I have experience in particular with Google AI Platform and Microsoft Azure ML. I am certified by Microsoft as an Azure Data Science Associate meaning that I am certified to provide cloud machine learning services using Microsoft Azure Machine Learning.