The only data curation and machine learning optimization platform you’ll ever need
While Big Data helped usher in widespread adoption of AI, it’s also responsible for its high price tag. After all, storing all that data is a real expense. Labeling it is onerous. Training models on those gigantic datasets is costly and time-consuming. And in business, “slow and expensive” is rarely a recipe for success.
Thankfully, the solution lies inside the Big Data you already have: it’s data curation. It’s Smart Data instead of Big Data.
Alectio’s platform uncovers this smart data. It’s like a wrapper that sits around your existing models and “listens” while it trains. It understands what data is actually helping your model—and what data is hurting it.
Why we created Alectio and how it works:
Big Data vs. Smart Data
The most pervasive misconception in data science is “the more data, the better.” This idea that getting more and more data will make models more accurate or that collecting more data can magically fix struggling models.
That’s simply not true.
The reality is that in any given training data set, only a fraction of it is generally useful. The rest is useless (redundant information, for example) or actively harmful (like data from faulty sensors or poorly labeled rows from your labeling provider).
Alectio is made to help you find the right data to train your models on. No matter what kind of data you’re working with, be it images, text, video, or audio, we can help. Alectio is data and model agnostic. In fact we don’t even need to see your data or your model.
In the world of machine learning, Goliath is Big Data. If you want to enlist your own David to fight back, we’re here to help.
Why less is more
Our latest blogs and resources:
The Machine Learning field is full of buzz: deep learning, LSTMs, generative adversarial networks; the list keeps going on and on and on. Some of the most promising concepts, though, stand at the other end of the spectrum. Active learning has to be one of the top...
Neural networks do evolve and change their predictions as they train and there’s been a recent effort to use those changes to understand the underlying training data better. One of the more notable attempts at understanding these changes is an ICLR '19 paper by...
As we enter 2021, active learning is perhaps the least understood and most underutilized technique in machine learning today. Its promise is simple and elegant: to reduce the overall records you use to train models without trading off accuracy. It’s an iterative,...