Imagine you’ve built the most advanced AI algorithm, a true feat of machine learning prowess. You push it to production, anticipating groundbreaking results, only to find it falters, unable to deliver on its promise. The likely culprit? A lack of robust DataOps.

Data operations, or DataOps, is more than just a behind-the-scenes function. It represents a critical pillar that can make or break your AI solutions. Without effective DataOps, even the most sophisticated AI algorithms can collapse under the weight of poor data management. Let’s delve into what DataOps really means and why it’s an indispensable asset for AI technology.

Unpacking DataOps

So, what exactly is DataOps? At its core, DataOps is the orchestration of obtaining, cleaning, storing, and delivering data in a secure and cost-effective manner. It’s a fusion of business strategy, DevOps, and data science, forming the backbone for many big data and AI initiatives.

Though originally coined within the realm of Big Data, DataOps has gained broader recognition and application across various domains. Effective DataOps practices ensure that data flows seamlessly from source to application, enabling timely and accurate insights.

DataOps: Your Key to a Competitive Edge

When it comes to outpacing your competitors in AI, DataOps is often more vital than the algorithm itself. Many AI solutions rely on standard algorithms fed with quality data, trained, and fine-tuned before deployment. Since these algorithms are largely uniform across the industry, the true differentiator lies in the data.

The effort required to achieve stellar results from high-quality data is minimal compared to the struggles of scraping by with mediocre data. Moreover, securing data at a lower cost than your competitors is crucial, especially for AI applications that demand continuous data influx. In such scenarios, the cost of data acquisition can become a significant economic burden.

Real-World Example: Paperflow

To illustrate these concepts, consider the AI company I co-founded, Paperflow. Paperflow processes invoices and other financial documents, extracting critical information such as invoice dates and amounts.

We decided to collect all data in-house and develop a proprietary system for data entry—an investment that, although costly initially, paid off in terms of data quality and control.

Contrastingly, our competitors relied on customers to input data when the AI failed to predict accurately. While cheaper, this strategy led to inconsistent and often incorrect training data, adversely affecting the AI’s performance.

Optimizing Costs with Efficient DataOps

To manage costs, we heavily invested in making the data entry system as efficient as possible. Through trial and error, we refined our processes, likely investing more in DataOps than in the AI itself.

We also focused on collecting only the data we truly needed, a common challenge in DataOps. Initially, we collected excess data, gradually narrowing it down to avoid redundant collection while retaining necessary information. This approach prevented us from discarding validated data, thus maintaining continuity.

Leveraging Probabilistic Programming and Active Learning

We developed methods to determine when our AI’s predictions were sufficiently reliable, utilizing technologies like probabilistic programming. Unlike standard machine learning algorithms that produce a confidence percentage, probabilistic programming offers an uncertainty distribution, significantly reducing error risks.

This practice of collecting data based on the AI’s uncertainty is known as active learning, a strategy worth exploring for anyone working on data operations in AI.

Tech Challenges in DataOps: A Nod to DevOps

On the tech-heavy side, managing data storage effectively presents its own set of challenges. Rapid data growth can strain scalability, making it imperative to involve a DevOps expert early in the architecture phase. Building on a scalable foundation is crucial to avoid constantly resorting to short-term fixes.