Hello Guy. We are elated to have you at AI Tech Park. Could you please tell us how DataOps.live came to fruition and give us a brief about your role as CTO at DataOps.live?

I’m delighted to be here! We came up with the inspiration for DataOps.live from our experience leading a tech-forward Systems Integration firm in England that developed advanced analytic solutions for larger enterprises, mostly using cloud data platforms and associated tools and infrastructure. In building these data applications, we noticed there was no methodology, such as how you would use DevOps to build software applications. So we came up with the concept of DataOps, which is essentially like DevOps for data. We further defined and established this concept by collaborating with a group of experts on a distinct website known as TrueDataOps.org, where we published the “7 Pillars of DataOps”. We also published the book DataOps for Dummies. And so DataOps.live was born!

As the term Data-Centric AI is on the rise could you elaborate what exactly Data-Centric approach to AI is?

Like machine learning, AI needs high-quality data that users can trust. In the world of generative AI, a wealth of world knowledge is required to be able to apply it across many use cases. These include chat-based experiences over domain-specific models that get fine-tuned for a specific industry or use case or both to the emerging agentic mesh. This is where AI makes semi-automated decisions (e.g., in the supply chain) to select a different supplier when expiring, such as a natural disaster temporarily disrupting transportation.

The data-centric approach focuses on providing correct training data to minimize false positives. Take image recognition as an example of how AI must distinguish between cats and dogs. When there is a single animal in the image, it will be correctly labeled as a cat or dog. If it is a group picture, the cat can easily hide in a group of dogs and is incorrectly labeled as a dog. Thus, focusing on the outliers in your training set improves the model’s quality, even if you do not change the foundational model.

In summary, the data-centric approach to AI is necessary to improve the trust, correctness, and reach of the AI models.

What is a Model-Centric approach? Put some light on the cons of the Model-centric approach and how it is falling back in the process of AI evolution.

The model-centric approach focuses on using the existing foundation models and improving their quality by continuously fine-tuning them, which results in smaller models that better fit the use case. This fine-tuning enhances the quality of the response for a given prompt, for example, in the area of data classification. Let’s say you want to classify the win-loss reasons for specific deals based on customer and sales representative interviews. If you prompt a foundation model, you will only get structured data as a response. Sometimes, you will also get the loss reason and prose as a response. And then again, sometimes you won’t get a loss reason back at all. In summary, you might have only 80% correctness.

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