Is data centric really the new oil or is it just the new buzzword for all things AI? After all, since its beginning, AI has gone hand in hand with data. Even here at Dataloop, since our inception, we’ve been data-centric. But let’s just take a step back and analyze the situation.
If we start with the most basic makeup of the AI system, we are dealing with the code or the models/algorithms, and their data. As Andrew Ng explained in his talk about “From model-centric to data-centric”, the way machine learning has progressed over the last couple of years is by teams downloading and trying to improve on standard benchmark datasets, spending most of their time on improving their code. But as we at Dataloop like to emphasize, what about the data? Let’s take a look at it from our perspective, and I’ll break it down for you. The first question we need to ask, then, is what is data-centric?
What is Data Centric?
We believe it’s all about the data and the quality of that data. And how do you achieve this? With 3 key pillars.
Pillar #1: Data Annotation Platform: Creating precise, accurate, and repeatable annotations are key to quality data. If you don’t correctly label your data, you can’t correctly build your model. If you don’t have enough data volume, you won’t be able to build a lasting model. But data annotation isn’t just about the volume and the quality of labeled data; it’s also about the type of annotations you’re using for the type of models you’re producing. After all, even if we were to stick to the “model-centric” approach, without ensuring best-in-class data labeling, our model wouldn’t go anywhere. This is the first step in creating a computer vision model: quality labeled data that can be scaled. It doesn’t matter if you’re doing classification, detection, or segmentation; if you create a computer vision model, you’ll need to annotate your data.
Pillar #2: Data Management: Data-centric is actually a data-stable approach. This means you are managing your data across its entire lifecycle. Even before you build your model, you need to track your dataset progression. How are you managing your datasets’ versions? How are you tracking the changes to your datasets? How are you searching through your dataset versions and filtering out key information? This is where a data management platform comes into play: you need to be able to filter, sort, clone, merge, version, and query your datasets down to the metadata level. Providing a single secure visualization layer for all your unstructured data allows you to better understand the plethora of accumulated data as your AI project grows. Robust tools assist data scientists, data engineers, and data operators to analyze data sets quickly and seamlessly.
Pillar #3: Automation & Pipelines: As you finally scale your AI project, the ability to automate your annotation and data management workflows is probably the most instrumental part of successfully maintaining a data-centric vs. model-centric approach. It’s not just about pushing your models to production; it’s about being able to pre and post-process your datasets. Being able to create human-in-the-loop data validation and being able to scale your work as you revise and optimize your ever-adapting models is key. Dataloop’s solution makes it possible for organizations to create custom data automation pipelines using a no-code drag-and-drop interface or through a developer-friendly Python SDK, weaving together human labeling tasks and machine learning workflows. This makes it easier to develop ML pipelines or create human-in-the-loop data validation that can be scaled to production. Moving through pre/post-processing and validation leads to continual and systematic optimization of your models in less time, with fewer resources.
Further reading: The Data Loop Phases
The Secret Ingredient to Data Quality
Preparing high-quality data for computer vision use cases is lengthy, complex, and expensive, creating a barrier to entry that many enterprises struggle to overcome.
Data is what makes or breaks your model. It’s not about the quantity but rather the quality. Therefore, your dataset needs to be diverse enough in order to provide your model with all the information it requires. Continuous validation from your production environment enables your organization to create a machine learning model that is constantly learning from its own behavior while adapting to new situations never encountered before. Dataloop’s 3 pillars can help you get to this goal. And there’s no other company on the market that can offer all 3 pillars under one roof. We serve one purpose: to ensure you succeed with a data-centric approach as you label data more efficiently and accurately while scaling to production – faster.
Dataloop is able to accelerate machine learning projects, by adding human validation in a continuous loop, improving the likelihood of success when shifting the model out of the lab and seamlessly transferring it to the real world.
Further reading: How Dataloop’s Approach To Human-In-The-Loop ML Boosts Project Efficiency
Summed Up
It all boils down to the most important point that all models rely on data and you can’t get them to production or have them function optimally without data. So in essence models and AI have always been data-centric but the approach to using data is now changing. Instead of Data Scientists talking about their models, they’re focusing more on the quality of the data that constructs their models. In our opinion it’s never been about locking into a buzzword (model-centric vs data-centric vs next centric); it’s all about the approach: data first!
