Deep Learning AWS GPU And How They Can Benefit Mankind?

Deep Learning (DL), a subset of Artificial Intelligence (AI) and Machine Learning (ML), has gained a profound rise in interest over the past few years. At its core, deep learning technology imitates the way humans gain knowledge, using neural network algorithms to duplicate human thought processes or intelligence for decision-making.

The use of cloud-based GPUs is growing exponentially in a variety of fields of research and commercial ‘big data’ applications, including Advanced AI-Driven Analytics, Visualisation, Machine Learning & Deep Learning.

In the future, cloud-based GPUs will play their part in keeping planes in the air, assist researchers in curing disease, helping to mitigate the impacts of natural disasters, and also making sure your autonomous car can drive you safely to work.

While GPUs have benefited from Moore’s law in the same way as CPUs, they’re not constrained in the same way. As GPUs have the ability to ‘spread’ across larger pieces of silicon, in very basic terms, a more powerful GPU can be achieved when the number of GPU cores are increased. This accounts for the monumental rate of progress being found in the GPU industry, and why progress isn’t looking to slow down at any point soon.

Today, Deep Learning has evolved into a powerful tool for organisations, enabling them to gain actionable insights into the expansive range of data they can mine and extract; even unstructured data such as text, speech and images.

Moreover, the exponential growth in computing power through GPUs has enabled deep learning to become increasingly feasible for enterprises of all sizes over the past few years. The proliferation of cloud computing and AI as a service, has further accelerated the momentum of this technology, helping it to expand to even smaller companies that can access such algorithms, without making a large initial investment.

With this in mind AWS (Amazon Web Services) EC2 G4 instances currently feature NVIDIA T4 Tensor Core GPUs, which provides access to one GPU or multiple GPUs, with different amounts of vCPU and memory. G4 instances provide cost-effective and versatile GPU instances to deploy ML/DL (Machine Learning/Deep Learning) models in both production and graphic-intensive applications.

Moore’s law has correctly suggested for over five decades that we can expect the speed and capability of computing to increase every couple of years with costs reducing proportionately.

GPU-powered Deep Learning tools use this raw computational power to take in huge amounts of data and break it down into individual insights. This means it can then be analysed and potentially actioned into a business decision. It allows companies to see patterns, trends, areas of growth and also areas of weakness and vulnerability. The better your business knows where it is now, the better it can forecast for where it needs to be.

For example, DL tools help manufacturers predict and react to supply chain problems, production challenges and equipment performance before there is an issue.

What’s The Difference Between AI & DL?

Whilst the goal of AI has always been to mimic and interpret human behaviour; Deep Learning (a subset of Machine Learning) enables computers to go further and solve more complex problems. Ultimately, better business decisions can be made with the help of AI and ML (Deep Learning) Business Intelligence tools.

Since the industrial revolution, technology adoption has long been the single greatest driver in making businesses more efficient, at a reduced cost. Whilst enterprises have routinely continued to invest in tech to drive up growth and profits, the global pandemic has sped up this trend exponentially by highlighting the inherent vulnerabilities of the manufacturing industry when confronted with an unexpected disruption to human labour.

Deep Learning also plays a role in predictive analytics, by harnessing a variety of statistical techniques that analyses both historical and live data, allowing systems to make predictions about future events and behaviours.

This sophisticated use of GPU power is used across a variety of industries, including telecoms, pharmaceuticals, defence, logistics, insurance, financial and far beyond.

Where are cloud-based GPUs being used?

Cloud-based GPUs have completely reshaped company IT architecture over the past few years, due to their ability to automatically scale computing resources, based on requirement. Over the next few years, the vast majority of organisations around the globe are expected to have systems that use cloud-based GPUs to meet at least some level of their computational needs.

As the cost structure for cloud-based systems works like a utility (in which there’s no upfront cost for the infrastructure) paired with the ability to scale with the growth of an organisation, this elasticity makes a good business case for itself as an increasingly appealing proposition for enterprises of all sizes.

Process-intensive tasks that can be accelerated by cloud-based GPUs benefit a variety of industries, including (but not limited to) insurance, financial, oil & gas, telecoms, DNA & genomics, pharmaceuticals, defence, logistics and many more…

The cloud-based GPU industry is set to grow exponentially over the next coming years, as the applications for GPUs widens up every day. As a specialised microprocessor, GPUs have the ability to take on intensive computations and entire datasets on board, enabling users to instantly interactively visualise, query and power data workflows over billions of lines of data.

The colossal computing power available from cloud-based GPUs can extract and process business intelligence from huge datasets with incredible speed and accuracy.

Predictive analytics is a part of business intelligence that’s becoming increasingly augmented by both Artificial Intelligence and Machine Learning, by using statistics and modeling to determine future performance and conclude potential outcomes, based on both historical and current data.

AI and ML can power predictive analytics to better identify patterns in data that can determine the likelihood of future emergence. This allows organisations to decide where best to focus resources, thus, be able to make intelligent predictions about the future.

About Author

 Technology & finance blogger and experienced Head of Digital –  Dave has worked in digital for 10 years, in client-side, agency-side, and freelance capacities.