So many ideas, but not enough labeled data
Does this sound familiar to you?
From our own experience as Data Scientists, we know the pains of gathering high-quality datasets. We incorporated our knowledge and the knowledge of many interview partners into our product that we believe will make the life of a Data Scientist just that much easier.
We believe that Labeling should NOT be ...
a one-time job
We faced that requirements can change in a Machine Learning project. This also accounts for labeling. That is why we built our platform to treat labeling as an evolving procedure. You can extract and add data at any time in the process.
If you haven’t labeled the data yourself, it often is hard to assess the data quality. Debugging your model is extremely difficult if you don’t know where poor model performance stems from. Labels come with insightful metadata, allowing you to understand label quality issues.
Labeling for few days can help you to understand the data at hand. But if you have to label for weeks or months, it becomes a painful task!
We make labeling as easy and convenient as possible, so you can save headaches for more difficult tasks.
Every person that labels data will develop some internal rules to classify the given data point. By expressing these rules as a labeling functions, you’re essentially building a labeling documentation.
There often are much more Machine Learning application ideas than available data sets. We eliminate that bottleneck. Define labeling functions, label some examples by hand and train weak classifiers to get a fast export of probabilistic labels.
Labeling tasks can be very time costly which delays the deployment of Machine Learning solutions. We aim to assist you in speeding up the data labeling process so you can build models in days, not weeks.
That's why we change that!
You and your team know your data best. That is why we built a lightweight platform that has much to offer but is still highly customizable to your needs. You can bring custom embeddings, enrich unlabeled data with attributes of your choice and then use them to programmatically label your dataset in a modern fashion. Every component of onetask was built with the Data Scientist as a user in mind.