Intergrid Insights

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AI Machine Learning Cloud Servers

When you consider the significant shift amongst businesses towards cloud services in recent years, the numbers don’t lie. More and more enterprises are committed to leveraging the cloud in a way that will yield maximum benefits, the likes of which traditional on-premises alternatives simply can’t offer.

Ease of set-up, low start-up costs, scalability and flexibility are just a few examples of why the cloud computing environment has become such an attractive proposition for so many businesses.

Machine learning explained

Machine learning initiatives are revolutionising the way businesses operate. They can be implemented for improved productivity and profitability, reduced labour requirements, and a better customer experience. The concept, which has remained something of a mystery to many, is now utilising the cloud to open the doors to Machine learning for businesses with even the most limited knowledge of data science.

For any readers unfamiliar with the term, Machine learning describes the use of artificial intelligence (AI) with a focus on building applications with the ability to learn from data, before making predictions based on patterns detected over time.

Previously, the activities and innovations undertaken by a business utilising machine learning models to improve its processes had to be conducted on-premises using a physical server. Barriers to implementation therefore included a lack of the specialist skills needed for the building, training and deployment of such models. In addition, the associated computational and hardware requirements carried high price tags.

The cloud and machine learning

Enter the cloud platform, and its ability to allow businesses to investigate how machine learning capabilities could provide the key to solving their business problems. This option reduces set-up costs and mitigates the need for advanced skills in machine learning theory, AI or data science – which can be difficult and expensive to come by!

The cloud provides a perfect environment for machine learning experimentation, and for scaling up as projects evolve through the development process, implementation, hosting, management, training, and general use.

Big player cloud solution vendors such as Google Cloud Platform, Amazon Web Services and Microsoft Azure are leading the way when it comes to cloud-based machine learning offerings. They provide clients with quick, easy and reliable access to the very latest, industry-leading hardware, whenever and wherever it’s needed.

Coupled with the right operating system, this type of access offers clients a myriad of machine learning options to improve their networks, depending on their specific needs. The list of outcomes is growing every day, and includes real-time chatbot agents, decision support, image and language processing, product recommendation engines, voice recognition, dynamic pricing tactics, customer segmentation, sentiment analysis, fraud detection, business intelligence and lots more.

Cloud providers also have the advantage of unfettered access to leading specialists in areas including scalability and security, and can therefore commit to constant innovation. What this means is that typically, and with the appropriate tools in place, the cloud provides the fastest means of prototyping and developing production-ready machine learning solutions.

Why not enjoy the best of both?

For some businesses moving their machine learning activities entirely into a cloud environment is not a practical or realistic solution. For instance, you may have sensitive information that cannot leave your physical data centre for compliance purposes.

However, it is becoming increasingly possible to utilise hybrid solutions. For instance, initial testing of a new machine learning model could occur on-site, before production-ready models are developed using the more powerful capabilities of a cloud hosted environment.

What’s the right machine learning cloud solution for my business?

If conducting your machine learning innovation activities in the cloud sounds like a good solution for you, it’s important to do your research. For instance, if data privacy is a key concern, make sure you choose a provider that can demonstrate particular expertise in this area.

Essentially, different providers offer different things. The best approach is to have a conversation with either your shortlist of preferred cloud providers, or your regular managed services IT partner, and articulate what you hope to achieve by using the cloud to support your machine learning efforts.

If you don’t already have a fully managed service provider, it’s well worth considering. It is the easiest way to ensure your IT network is built and maintained in a way that meets your distinct business needs.

In becoming an extension of your own team, your external IT partner will not only work to identify and resolve issues effectively, but will also proactively look for opportunities to make changes to your network that will lead to improved efficiencies, enhanced productivity, and an all-round better experience for your people and customers.

The right managed services partner will have the right insights into your business needs to be able to suggest the correct machine learning cloud server solution for you, and explain the relationship between data science and machine learning, and the value and positive outcomes for your business.

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