Cloud AI consulting

azure data scientist associate 600x600We are able to work with any cloud AI environment to develop and deploy machine learning models to production. Fast Data Science is in the Microsoft Partner Network and the director, Thomas Wood, is certified by Microsoft as an Azure Data Scientist Associate.

We can provide cloud machine learning consulting on the following platforms:

  • Microsoft Azure ML
  • AWS Sagemaker / AWS machine learning
  • Google Cloud Platform

NLP on Cloud

Natural Language Processing tasks are often resource intensive and require high performance computing environment such as GPUs, if they rely on deep learning technology.

It is impractical for every organisation to purchase and maintain the necessary hardware and for this reason cloud computing has become essential for natural language processing applications.

There are a number of text analytics vendors on the market offering both pre-trained NLP models and the ability to build your own custom model. As of 2020, the main competitors in the market are

cuadro cloudml

Due to market forces, prices and offerings tend to be broadly similar across vendors. At Fast Data Science we are prepared to look into and evaluate any cloud NLP provider according to your organisation’s needs.

Why run AI and NLP on cloud?

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.

Machine Learning Consultant
2-min
MANO2-compressed
3-min

Cloud machine learning for healthcare

Cloud solutions are becoming increasingly used to bridge the final mile between patients and healthcare providers, as it is hard to deploy machine learning solutions on-premise at all locations where patients will receive treatment. The offerings of cloud AI are improving access to care in the case of geographically distributed populations.

Because medical data is highly sensitive, it is important to ensure that data does not leave the jurisdiction of the patient. For example, Microsoft Azure has a system allowing machine learning solutions to be deployed across regions without sending data between regions, and similar solutions are available with competing cloud providers.

Advantages of Cloud AI versus On-Premise Solutions

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.