We are able to work with any cloud AI environment to develop and deploy machine learning models to production. The manager, Thomas Wood, is certified by Microsoft as an Azure Data Scientist Associate.
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:
- you don’t have huge upfront costs of buying hardware that you may later need to upgrade or which may become obsolete.
- somebody else takes care of servers, IP addresses, SSL certificates, DNS, and so on, so you don’t have to.
- you have the flexibility of being able to upgrade or upscale your hardware such as GPUs at the click of a mouse when demand grows, similarly you can downsize your solution when demand shrinks.
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.
We have experience in particular with Google AI Platform and Microsoft Azure ML. Thomas Wood is certified by Microsoft as an Azure Data Science Associate meaning that he is certified to provide cloud machine learning services using Microsoft Azure Machine Learning.