A look at scalability and Azure Part 3 – Machine Learning and Analytic Engines A look at scalability and Azure Part 3 – Machine Learning and Analytic Engines
In this third part, I will cover some of Azure's more advanced features, such as its machine learning and analytic engines. A look at scalability and Azure Part 3 – Machine Learning and Analytic Engines

Following on from Part 1 and Part 2 of this series, where we looked at Linear Scalability and the Scale-Out approach.

In this third part, I will cover some more advanced features, such as the machine learning and analytic engines that Azure currently offers.

As this is new to the market, many organisations do not know where to start with it. Businesses are constantly hearing that these are the types of solutions that will drive their businesses into the future, and this will make them stay relevant in the market.

We often hear questions like:

  • What is the cost?
  • Where do they start?
  • Do they need to invest in new infrastructure?

Personally, I would look at a very logical approach to something like this: if most analytic engines are currently cloud based applications, it would make sense to move your data to the cloud. That way, it’s easier to learn and leverage off the services that are built in the same datacentre that your data resides in.

I believe that all these extensive cloud-based services (starting from very basic to advanced), contribute to the true definition of the Scale-Out approach (going far beyond the hardware capability).

Scaling out has no limit and allows organisations to change the way they problem-solve and generate new ideals and marvels that may (and are) currently revolutionising new business outcomes.

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