Artificial Intelligence (AI) is already impacting almost every aspect of our lives and its influence is set to grow exponentially over the next few years. According to Gartner, AI augmentation (the human-centric partnership of people and AI) will create an additional $2.9 trillion of business value and 6.2 billion hours of worker productivity by 2021.
Granted, those forecasts were made last year and a lot has changed since then. The way 2020 has unfolded so far has caught all of us by surprise and most business strategies are frantically being revised or rewritten.
Does that mean that the AI bubble has burst and the growth trajectory has suddenly been derailed?
The answer has to be an emphatic “NO”, although the next steps for many organizations may well be more cautious and pragmatic.
IDC predicts that spending on AI will continue to grow this year driven by the need to improve customer experience, help employees get better at their jobs, and accelerate innovation. As we emerge from the current global emergency, every organization is going to be firmly focused on efficiency, profitability and competitiveness. And that’s where AI pays dividends.
Take a look at the Gartner’s Hype Cycle for AI and you’ll see that AI is inextricably linked to so many other influential technology trends. It’s already a key factor in data analytics and decision automation; advanced healthcare diagnostics; autonomous vehicles; smart cities and systems; robotics; cybersecurity and threat detection; natural language processing (NLP); and many other fields.
That’s why AI will continue to be vitally important.
Making a success of your AI project
This doesn’t mean that everything related to AI will be plain sailing. Far from it. Over a quarter of all AI initiatives have already failed or been abandoned. Why? Often it’s down to the scarcity of appropriate skills or due to data-related difficulties. However, the challenge of building suitable development and production environments is also a primary reason for the collapse of many projects.
Building an integrated infrastructure to properly support AI projects can be a complex and arduous undertaking, with countless options to be weighed up and decisions to be made along the way.
Data scientists ideally require a ready-to-go environment for a fast start, enabling them to rapidly design and develop their AI models. IT operation teams need an AI ecosystem they can easily deploy, configure and monitor. It must have all the building blocks and tools to support the demands of the data scientists. Both of these groups want to make life simple, get started quickly, avoid wasting time or making mistakes, and minimize the risk of failure.
And that’s where SUSE comes in.
Solving IT infrastructure challenges with innovative open source solutions is in our DNA. Hence, we’ve drawn on our experience, vision, expertise, and industry partnerships to launch comprehensive AI infrastructure solutions to satisfy the requirements of all the stakeholders within the business. They include the following elements:
- SUSE AI Orchestrator.
A self-contained environment for designing AI systems that has everything needed to develop, train and push to production Machine Learning models. It includes everything needed to get you up and running, with a choice of hardware, operating systems, AI development frameworks, editors, templates, and monitoring tools. All of this is packaged for a simple 3-click automated deployment and with a web-based UI for control and management. While some of the components are available today, the full version of the SUSE product is scheduled for availability in the Fall 2020. SUSE AI Orchestrator reduces time to production for data scientists with prototyping and remote deployment, while enhancing monitoring of remote workloads for operators.
- AI “Stacks”.
The stack represents an optimized infrastructure that perfectly fits the customer’s hardware architecture. Platform stacks are guideposts for what’s needed for when AI solutions are ready for production deployment – including all the stability, scalability, and performance required. SUSE is collaborating with trusted industry partners such as Dell, AWS, Intel, AMD, Arm, and NVIDIA to provide fully certified reference architectures for these comprehensive production-grade ecosystems. These stacks complement the SUSE AI Orchestrator with a ready-to-go AI platform.
- AI Packages.
These are pre-built and ready to use open source AI frameworks, platforms, packages and tools available from SUSE Package Hub to deploy and use as part of the SUSE AI solution. These include packages such as TensorFlow, PyTorch, Caffe, OpenCV, ONNX, ArmNN, and R-Studio.
SUSE has an ambitious roadmap is in place to deliver against this infrastructure vision. The focus is on freeing organizations from the time-intensive and tedious tasks that hold them back from making their AI projects a reality.
I’m convinced that AI will continue to grow in importance for businesses in every vertical segment. It offers an immediate and realistic path toward the business innovation and improvements that will be so vital in overcoming the challenges in front of us all.
If you’d like to learn more, please check out the links below:
- Machine Learning with openSUSE
- Artificial Intelligence: Do it with SUSE!
- SUSE High Performance Computing
Thanks for reading!
Jeff Reser @JeffReserNC