Alectio FAQ

There is a better approach to preparing and managing your training data for production-ready AI.

Active learning, reinforcement learning, meta-learning, and deep learning are used in combination with our data labeling solutions and advanced analytics to provide you the most useful training data for your models. This is how Alectio does Data Prep Ops.

A Flexible Platform for Today’s AI

Alectio presents a new way of approaching how training data is managed and used.

Proactive Data Curation

The old way was about acquiring more training data. The new way is about selecting the most useful data for your models.
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Reactive Data Filtering

Alectio can listen to your models in real-time and learn what data they need to perform at their expected best.
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Alectio Data Annotation

Find a trusted partner within our Labeling Marketplace, use our Auto-Labeling capabilities or do both. The choice is yours to get the most useful data for your AI.
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Data Prep Advanced Analytics

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What is Data Prep Ops?

Data is the key to AI. Having accurate data, and the appropriate amount of data, can only be managed through a proper Data Operations strategy? Download our guide to learn what Data Prep Ops is, and how it can help you present the best and right data for your models.
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Frequently Asked Questions

How can using less data lead to better model performance?

That’s definitely a pervasive belief. But remember something we said up above: all data is not created equal. Some of it is really useful for model training. Some of it less so (redundant data, for example, can cause overfitting). Some of it is actively harmful (mislabeled data, for example, can cause serious confusion).

What kind of data does Alectio work with?

We can work with virtually any kind of data, though our approach excels especially with images. That said, we can work with virtually any data type because our tech learns from the metadata (log files) generated from the training process itself.

Will Alectio help me with feature engineering?

Our technology identifies which records are the most impactful and useful to a model, not which features should be used in a model. That said, since we can identify which data is useless to a learning process, it can occasionally be used to find weaknesses in the model itself, which can in turn help with feature engineering.

What if I don’t have a model yet or I’m still developing it?

In most situations, usefulness is actually a function of the use case and the data versus the model itself. Think about a facial recognition problem. Regardless of if you’ve selected a model, data without a person in it or with bad resolution is going to be less useful than other data. We can uncover that without knowing what model you’re using.

So data usefulness isn’t model-specific?

Usually not! Our research shows that usefulness is data-specific, not model-specific. For example, data uselessness is usually due to either redundancy or irrelevance, and while irrelevance is use case specific, redundancy is a more general concept. Data hurtfulness is also fairly use case agnostic. You can read a bit more about that here.

Can I still use human-in-the-loop to curate my data?

Of course! Many companies have dedicated teams focused on data curation these days. The issue is that people don’t understand how models work, especially black box models like deep learning. Having them decide which data matters often amounts to wild guessing and can inject biases into your data. At Alectio, we sometimes say we give the model a voice. It decides what data it needs to learn.

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