Tap into the Ever-Growing Interest in Artificial Intelligence

Introduce customers to advanced server technology that’s ideally suited to transformational workloads

When Dell acquired EMC in September 2016, it didn’t just gain the industry’s leading enterprise storage vendor. Suddenly, Dell was also able to tap into a rich vein of Machine Learning expertise in EMC. This capability now sees Dell EMC adopting a leading position in the fast-growing Artificial Intelligence market, offering technologies and services that span the edge to the core to the cloud.

Thanks to the inherent product optimization and consultancy expertise offered by the combined company, Dell EMC partners are now ideally placed to benefit from customers’ ever-growing interest in the potential of Artificial Intelligence (AI) to deliver business growth.

Industry analyst Moor Insights & Strategy reports that worldwide demand for AI technologies is surging, with organizations of all sizes identifying possible application benefits in terms of productivity, revenues and product improvements1. In fact, IDC forecasts that total spending on AI will ramp up to tens of billions of dollars by the early 2020s.2

Tap into the interest in AI initiatives

AI is a transformative technology that has the power to change the way organizations interact and add intelligence to many products and services through new insights that were previously hidden in vast pools of data. The breadth of AI applications continues to grow, as progressive companies continue to advance and explore the capabilities of the underlying technologies of Machine Learning and Deep Learning.

AI is a branch of computer science that deals with the simulation of intelligent behavior in computers and the capability of a machine to imitate human intelligence. It can cover everything from characterizing something as ‘simple’ as recognizing an object in an image to applying reason and ethical values in problem solving – something far beyond current capabilities within the field.

Machine Learning uses statistical analysis modeling for use cases such as resource allocation, predictive analytics, predictive maintenance, text classification, bioinformatics, trend discovery, forecasting, face detection and pricing. These ‘classical’ statistical approaches are generally well suited for uncovering trends and categories in numerical data, and do not require massive training datasets or hardware accelerators.

Deep Learning, meanwhile, is all about discovering and modeling complex unstructured data for perception and recognition purposes. It’s now widely used for computer vision, speech recognition, natural language processing, social network processing, autonomous driving, image processing and classification, and financial market modeling.

A Smart approach to driving insights and improvements

In its report on the subject1, Moor suggests that interest in the potential of AI is primarily driven by two key motivations:

1. To improve operational efficiencies – what it refers to as ‘Smart Operations’; and

2. To enhance products and services through data-driven insights and use of unstructured data types, such as voice and images, to enhance human-machine interaction – what it calls ‘Smart Products and Services’.

Smart Operations includes everything from e-commerce product recommendation engines to cyber security, customer sales and support chatbots, financial trading, fraud detection, enhanced public safety services and supply chain optimization.

Smart Products and Services can be found in the fields of medical diagnosis and treatment, drug discovery, hospital clinical care management, autonomous vehicles, drones, consumer electronics, and threat intelligence and prevention.

Over the next decade, AI is predicted to have an impact on virtually every product and business process. This means there’s more than likely something that your customers are currently doing that could benefit from AI input to enhance efficiencies and/or outcomes.

AI requires modernized compute with optimized server technology

Across all business sizes and sectors, it’s widely acknowledged that modernizing a company’s compute infrastructure is an essential first step towards implementing AI initiatives. Because AI requires specialized and optimized compute.

Organizations wishing to implement AI initiatives are finding that they need to invest in the advanced level of computational horsepower that Machine Learning, especially Deep Learning, demands – as well as high-bandwidth, low-latency networking and storage capabilities.

While modernization does require some investment, a recent Forrester Consulting research study3 reported that an infrastructure upgrade will pay off not only in financial ROI terms, but also in additional business value, such as improved customer experience and IT operational efficiency.

There’s no denying that AI costs are significant, but so are the returns. Despite hesitations around the upfront cost, 51% of companies in the Forrester study reported that they’re expecting an ample return of between 2x and 5x in terms of their AI investments and the additional business value.

The inefficiencies of outdated infrastructure

The Forrester survey found that the largest challenge to AI in the data center was antiquated infrastructure that made it difficult for the IT organization to support the business in an agile manner. Enterprises especially called out the inefficiencies due to a lack of server and workload automation, as well as insufficient security and legacy software that could not be upgraded.

The latest Dell EMC PowerEdge servers, powered by next-generation Intel® Xeon® Scalable processors, are the bedrock of the modern data center – with scalable business architecture, intelligent automation and integrated security built in at every step.

As a Dell EMC partner, you’re ideally placed to promote servers such as the brand new Dell EMC PowerEdge R940xa (optimized to run AI and machine learning workloads) and the Dell EMC PowerEdge R840 (ideal for turbocharged in-database analytics), which will help your customers to maximize performance and optimize efficiency right across the business – with no compromises.

We’ve also recently developed pre-configured ‘Ready Bundles’ for Machine Learning and Deep Learning at scale. These are designed to simplify the configuration process, lower costs and speed deployment of distributed multi-node Machine Learning/Deep Learning clusters – to make life easier for AI practitioners at every stage of their implementation.