Gartner predicts only 15% of AI solutions deployed in 2022 will be successful. 15%! That’s horrendous. And yet, you know that for your organization integrating artificial intelligence (AI) into your business operations has become imperative. Covid19, remote workplaces, and AI apps like ChatGPT have only further accelerated what was already in high demand. But, before you go jumping into the latest AI tech and software for your company you don’t want to end up like the 85% that could deploy their solutions successfully.

Companies implementing AI often don’t realize the hidden costs that often cripple them. This is a much greater threat than the fear of missing out (FOMO) than what you should really be afraid of – flushing your money down the drain with bad AI implementation.

It’s much better to have a small clear and focused goal for AI transformation than to use sweeping changes across the board.

So, don’t worry. If you haven’t done anything yet, that’s okay. There are a number of huge problems most AI experts aren’t going to tell you about. They’re not going to tell you because.

• They don’t know it exists

And if they did…

• They don’t know what to do about it
• They want to keep your business.

For us at MDCS.AI, it’s better if you know what you’re getting into before committing to something that could hurt you in the long run. This is why MDCS.AI will always be upfront and honest with you. Even if it means we are losing your business. We’d much rather have you know what’s happening and walk away than lose both our time and money. The main reason for this is when we work with an organization, we’re looking to form a long-term partnership where your success is our success.

So what are the Basics?

The basics of a good AI platform start with relevant hardware. If you don’t have that, then you don’t have a place to begin. Now add to that your brilliant business concept that aligns with seamless software integration.

When you look at your traditional IT infrastructure it will probably fall short of what you need. It may have served your company well till now, but if you’re looking to implement AI, you’ll probably need an upgrade.

Simply said, your traditional IT infrastructure isn’t properly equipped to handle the demands of AI workloads.

A good way to look at it is a city’s transit infrastructure. Sure, a horse and buggy can trot down an interstate highway, but it’s more suited to faster vehicles and larger transport.

It’s the same thing with AI architecture. You’re going to be “transporting” massive amounts of data at much higher speeds. The kinds of speeds that make your normal IT architecture look like a horse and buggy. Just like resigning a city’s transit, your AI infrastructure will need the same kind of comprehensive redesign. GPUs are an absolute must for AI computations. Whereas most PCs need a faster CPU, AI won’t settle for anything less than GPU accelerators. GPUs provide the processing power required for efficient AI computations, similar to high-speed vehicles on a transport system.

AI applications are data-hungry applications and need accelerators. This means adopting a different approach to external storage, networking, and internal hardware choices, such as PCIe vs NVLINK. In the realm of AI workloads, the use of AI workloads relies on massive datasets, measured in terabytes and petabytes. These demand a different approach to data management, storage, and networking. Your infrastructure has to handle this immense volume of data efficiently. You should probably consider implementing high-performance storage solutions and alternative networking options, such as InfiniBand vs Ethernet.ﾠﾠ

Here’s another reason why these sweeping changes and massive upgrades don’t often work.

As you’re redesigning your AI infrastructure, it needs to be far more scalable than most platforms currently operate at. In this way it can adapt to ever-increasing workloads that go along with the dynamic nature of AI.

How do I optimize my AI workloads?

These concepts may be unfamiliar to traditional IT departments, this is similar to managing traffic flow with advanced systems in a transport network. You’ll want containerization (the ability to run applications semi-independent of the OS), ready-made pre-trained AI models, workload managers, reference hardware architectures and Linux-based MLOps that play vital roles in optimizing AI workloads.

MDCS.AI places extra emphasis in these areas, providing the expertise and support necessary to embrace containerization, workload management, and MLOps as integral components of your AI infrastructure.

MDCS.AI is your dedicated partner in transforming your AI infrastructure. With our comprehensive range of solutions and services, MDCS.AI assists in navigating the complexities of redesigning your IT architecture for AI workloads. Just as a reliable transport system requires collaboration with transport authorities, MDCS.AI offers the expertise to optimize GPU accelerator utilization, implement containerization and workload management, address data intensity and scalability challenges, and bridge the skills gap within your organization.

When you’re ready to embrace the potential of AI and embark on this transformative journey join MDCS.AI to drive innovation and success in the digital AI era.

Michel Cosman
CTO – MDCS.AI