There’s nothing new under the moon, and the hustle around AI lately only points to the mass adoption of this technology. For more than 70 years, people have been trying to convince themselves, computers, and robots that there’s any chance to make the last ones capable of self-educating and making decisions in a changing world. As humanity, we made a few steps in making computers much more independent, forcing ethics and philosophy to make amends to their codexes faster than technology evolves.
But… Do we really progress THAT much? As DevOpses, we always analyze the underlayer, the AI infrastructure that stands behind the solution, tool, or platform. Read this article to find out the limits and true achievements of the modern infrastructure AI projects can possess.
Artificial Intelligence has many definitions reflecting changing meanings, expectations, and scientific achievements. Yet, regarding what is AI infrastructure—the answer stays more or less the same: it’s the hardware and software suite connected to the network or another ecosystem to perform tasks that typically require human intelligence.
What are those human intelligence functions we expect from AI?
You might say that all these functions are not always available altogether in a single human being—and you’ll be quite right. So, the realization of different techniques, models, and functions requires various AI infra and embodiment in various solutions. Let us name a few for your convenience and to connect the theoretical basics with real-world implementations.
We observe two contrasting types of people depending on how they estimate the commonness of artificial intelligence. One doesn’t mention it at all, while another sees AI EVERYWHERE, adding bits of magical thinking to real life. So who’s right?
Let’s evaluate the abundance of AI-powered solutions, tools, and platforms and then review the market share and growth trends for this domain.
To sum up, AI is pretty much everywhere if you’re using a smartphone and live in a digitalized environment. But can we say that the market is saturated in this case?
So, there are still many niches to fill in. Real-time masks for social media, HR advisors, another text-reliable genius writer—everything you can imagine. Teams like Dysnix help to fully implement those ideas in life even for companies without AI/ML background.
And as AI can’t be imagined without machine learning, let’s clarify this one as well.
The main point of this paradigm is to make computers act by following the data and conclusions derived from it rather than relying solely on predefined algorithms and instructions made by humans. ML helps to learn from patterns, adapt faster to changed circumstances, and boost performance without explicit reprogramming.
ML frameworks today are a blooming and fruitful field of pre-built tools, libraries, and interfaces to simplify data preprocessing, model creation, and hyperparameter tuning. Examples to play and get acquainted:
The realization of ambitious AI in infrastructure demands other types of “building blocks” than traditional IT ecosystems can offer.
AI models, especially deep learning networks, require vast amounts of processing power, storage, and memory to process and analyze large datasets. Traditional CPUs, which are designed for general-purpose computing, struggle with parallel data processing tasks that AI workloads demand. So, the AI infrastructure ecosystem demands another way of organizing to be efficient.
Here’s a brief comparison of traditional infrastructure and the one that suits your future artificial intelligence applications.
There are core differences between an AI infrastructure solution and a traditional one. Most of them influence how infrastructures process, scale, store, and act within the data flows and connections they possess.
With a reliable DevOps team, your transition to the modernized infrastructure can be less stressful, more manageable, and be your wisest investment.
AI/ML engineering is far more experimental and iterative than traditional apps. The flowing nature of data and the looped lifecycle of ML models add complexity to the process, making them more challenging to build, operate, and maintain.
AI models can "drift" over time, meaning their performance deteriorates as the underlying data changes. Managing such maintenance on a large scale can be resource-intensive. Constant analysis of an infrastructure that supports CI/CD and model monitoring can be complex and costly.
AI workloads require specialized hardware like GPUs and TPUs in various configurations and ecosystems. Managing a multi-hardware environment that mixes different GPU models, cloud resources, and on-premise machines can create inefficiencies.
Balancing and optimizing across this fragmented hardware environment requires careful planning and resource allocation.
Running AI workloads—especially during model training—can be incredibly expensive, particularly when using cloud services. Without proper cost optimization, organizations can easily exceed their budget. AI infrastructure must include cost-monitoring tools and budgeting strategies to ensure the scalability of operations without compromising financial sustainability.
But there’s nothing unrealistic about solving these problems. Talented DevOps and MLOps engineers can tackle them right away.
Being a DevOps practitioner dealing with AI/ML infrastructure is challenging (making an analogy—it’s hard to be a good parent and quite easy to be just a parent). This is not because of the technical part with all that data collection, model training, retraining, and deployment contests (oh, this part is the easiest for the Dysnix team), but because it involves bringing your project into the bigger picture, where that exact AI should serve.
There’s always a bit of risk and/or surprise, especially when it comes to changes that come after time. That’s why we continuously support our AI projects to ensure they align with their ecosystem, which can instantly alter because of e.g. a plane crash somewhere in a vulcano or climate change.
And thus, AI is still hardly dependent on the underlayer it grows on and people taking care of it. There are technical frames it can’t cross, for now, there’s a speed of light! Anyway, observing what it can do with even these physical frames is fascinating. Join us in the first rows and on the stage of this show 🙂
