The Frontier of new Machine Learning and Cloud Computing for Business


Recently, cloud computing has come to have an effect on every single industry. The cloud has become a ubiquitous term but it is actually a very diverse field. Many people are not really sure what the cloud even is.

One helpful method to get to grips with it is to think of cloud computing as a new operating system like Windows or Linux. Instead of creating software for each OS, it is now possible to design an application to function natively on the cloud
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Designing for the Cloud

Designing natively for the cloud has lead to an increase in the popularity of serverless technologies. This includes examples like the Amazon Web Services (AWS).

Rising at the same time as serverless technology is new machine learning platforms such as AWS Sagemaker. Sagemaker usually functions by provisioning a Jupyter Notebook in order to look through an AWS ecosystem's data sets. 

Advancements in AI

The next stage in artificial intelligence is to take the native capabilities of the cloud and use them to enable even higher-level technologies. For example, there are many people who still assume that adding more memory to an applications means having to physically travel to a data center before having to insert a physical disk into a machine rack. This is a common method used by companies that are looking into developing artificial intelligence or machine learning systems.

It will not be long before working in this manner has become entirely obsolete. Instead, taking advantage of platforms that utilise cloud-native technologies will enable companies and organisations to gain incredible advantages but they must move in this direction before it is too late. It is capable of taking operations that should require months and reduce them to a matter of hours or possibly even minutes 

Benefits of Cloud-Native Technology

The good news is that taking advantage of these cloud-native platforms is not even difficult. Any major cloud service is going to allow people to sign up for free so that they can run some sort of trial. For example, when using Amazon Web Services (AWS Sagemaker to be precise) there are many example notebooks available. These can either be used as they come or changed to fit the needs of the company.

One of the major challenges preventing organisations from adopting the technology is the initial buy-in. Any employee can try to demonstrate the value by taking it upon themselves to create a prototype at their own expense. After proving that the methods are viable, they can be shown to the organisation who can then make the purchase with much greater certainty that they are doing the right thing. 

Another Use for Artificial Intelligence

This is not the only current trend. Another is that corporations are using AI APIs that can be bought with off-the-shelf functionality that also combines with the frameworks of serverless applications. These AI are excellent for use in solving sophisticated problems such as image and video processing and analysis or natural-language processing.

Between these technologies and the availability of cloud-based serverless architecture, it is now possible for more complex applications to be developed more quickly by smaller teams that have access to fewer resources.


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