How to find the right infrastructure to maximise your AI’s potential
- By: Ratan Dargan
The ongoing global crisis pushed the business world into an accelerated path of digital transformation. During the early half of last year, I found myself standing back in awe at the amount of traction digital transformation was gaining in the market. I always knew that it will, but never imagined it to happen so fast. This made me wonder, what are they going to do with all the data that is going to be generated now?
I mean, one of the first changes that would come from embracing a world of digital possibilities would be the data explosion. But having a lot of data doesn’t mean you are going to gain a lot of insights from it. That would require the right analytical infrastructure. More importantly, it would require a data driven approach.
Making sense of data
For businesses today, the ability to use analytics to garner insights from the massive volumes of data they produce can positively impact their success. Manual analytics might have been adequate to get the job done in the past, but that won’t work as data keeps increasing in complexity and volume. Artificial Intelligence (AI) and Machine Learning (ML) can help when this phase comes to pass. I mean, these technologies can help create data analytics systems that can handle complex queries and massive volumes of data, near real time. But there is a catch.
Is your IT infrastructure built to be AI ready? Your AI’s success would undoubtedly depend a lot on having the right infrastructural foundation. The lack of dependable infrastructure is one of the primary reasons many enterprises have made fewer gains from their AI investments than expected.
Without the high-performing, scalable storage solutions that can house large swathes of data, as well as the processing power to review and categorise it, AI projects are bound to lose steam. Traditional on-premises infrastructure often falls short of delivering data pipelines that are optimised to the needs of AI applications, resulting in data bottlenecks, higher costs, and frequent data losses.
So then, from an infrastructural perspective, what key infrastructural requirements would you need to have in place to maximise the potential of your AI? There are five key areas to consider:
The computation and inference made during your AI’s training phase play a critical role in determining its overall performance. To process large datasets, you need to develop sizeable AI models that would, in turn require more intensive training phases. Hence, depending on the amount of data and the size of your model, the computing power you need at your disposal would also vary.
A CPU-based environment will be able to handle basic AI workloads without much strain. But accelerated workloads, like in the case of deep learning, involve larger datasets and scalable neural network algorithms that require more robust computing.
Scalable storage solutions are an indispensable part of every modern AI infrastructure. Organisations need to identify a suitable storage solution depending on the amount of data their AI applications would use and generate. Today, most businesses opt for cloud solutions since they offer seamless data control, management, and sovereignty. When there is data sensitivity and privacy challenge, hybrid models will be the best options.
Another crucial building block of AI infrastructure in today’s high-speed digital environment is networking infrastructure. Irrespective of being implemented on the edge or the cloud, AI systems need to send and receive data at high speeds to contribute effectively to decision-making in real-time. As AI systems often handle copious amounts of data, the networking infrastructure should handle large bandwidths and have low latency.
Cyber-attacks have unfortunately been on the rise in recent times, alongside the accelerated pace of digital transformation. Cyber attackers often target sensitive data. Since AI projects might require the analysis of medical records, personal information, and financial data, it is imperative to have state-of-the-art cybersecurity solutions to protect your AI infrastructure.
Focusing on solutions that can effectively deliver on these four aspects of infrastructure can significantly impact the success of your AI project.
The change is here
But with the ongoing economic slump caused by the crisis, how can you find the right solutions that deliver the high levels of performance you require while being cost-effective? I think this is where the as-a-service model offered by the likes of HPE Greenlake Cloud Services can usher in radical change. See, the advantages of opting for HPE Greenlake are multifold. For one, HPE Greenlake comes with an expansive list of curated solutions that include hardware, software, and expertise, which are all wired towards simplifying IT and unlocking greater value from your data. Next, it combines the capabilities of both the public cloud and on-premises IT to bring you the best of both worlds. Additionally, its outcome-based, pay-as-you-go model lends you greater financial flexibility, letting you scale up or down based on your requirements. If you are considering adopting a state-of-the-art solution that delivers on all ends, HPE Greenlake is the way to go.
If you’d like to learn more about the digital services and solutions that can bolster your data initiatives, you can get in touch with our team at ThoughtSol. Our skills and domain expertise are geared towards helping you overcome today’s digital challenges to meet your transformation goals. You can leverage our support to optimise cost, increasing efficiency, simplify operations, and get your business ready for the future.
I co-founded ThoughtSol Infotech in 2014 with the objective of empowering enterprises with IT infrastructure to meet modern business needs. With a resolute focus on quality processes, modular frameworks and rapid implementation of IT solutions, we deliver a wide variety of converged infrastructure services for the 21st-century enterprise.