Build state-of-the-art ML Pipelines
with Active Learning
Lightly selects the subset of your data with the biggest impact on model accuracy, allowing you to improve your model iteratively by using the best data for retraining.
Build state-of-the-art ML Pipelines
with Active Learning
Lightly selects the subset of your data with the biggest impact on model accuracy, allowing you to improve your model iteratively by using the best data for retraining.
Scale
Select the best 1% from millions of images or videos
Lightly's algorithms can process lots of data (e.g., 10k videos or 10m images) within less than 24 hrs
Automation
Use our API to automate the whole data selection process
Connect Lightly to your existing Cloud buckets and process new data automatically
Science
Use state-of-the art active learning algorithms
Lightly combines active- and self-supervised learning algorithms for data selection
Features that lift you to the next level
We help customers to have up to
90%
less labeling costs
Data redundancy can not only hurt model performance but also create significant costs for data labeling, storage, and compute. Don't waste your money on bad data!
- Tired of only selecting 1 frame per minute to reduce the data load?
- Want to get randomness out your data?
- Manually picking doesn't give you the best result?
Lightly will be able to help you with that and will make selecting the best data easy for you
20%
better models
Selecting training data that is difficult for your model can yield significant gains in accuracy. Use active- and self-supervised learning approaches thanks to Lightly.
- Send the best subset of your data to labeling at the click of a button
- Trigger retraining and model deployment
- Automatically build datapools and datasets
2x
faster retraining cycles
Managing your data and machine learning pipeline efficiently saves a lot of time and reduces errors. Leave hacky in-house solutions and scripts behind for a scalable and reliable solution.
- Send the best subset of your data to labeling at the click of a button
- Trigger retraining and model deployment
- Automatically build datapools and datasets
- Take the human error factor out of your data curation equation
- Get access to cutting-edge data curation technology
- No need to implement research papers yourself
We trust in hard math and base Lightly on it
Ask our customers
“Lightly gave us transparency to a part of the ML development that is a black box, data. Furthermore, Lightly enabled us to do Active Learning at scale and helped us improve recall and F1-score of our object detector by 32% and 10% compared to our previous data selection method. We finally saw the light in our data using Lightly.”
Gonzalo Urquieta
“Lightly enabled us to improve our ML data pipeline in all regards: Selection, Efficiency, and Functionality. This allowed us to cut customer onboarding time by 50% while achieving better model performance.”
Harishma Dayanidhi
"Lightly is hyper-focused on finding thousands of relevant images from millions of video frames to improve deep learning models. The Lightly platform enabled us to build models and deploy features more than 2x faster and unlock completely new development workflows. I can recommend every MLOps team with a lot of data to integrate Lightly."
Isura Ranatunga
"I was truly amazed once we received the results of Lightly. We knew we had a lot of similar images due to our video feed but the results showed us how we can work more efficiently by selecting the right data"
Alejandro Garcia
"After training a model on the filtered data suggested by Lightly, I saw a dramatic increase in performance on our key metrics. Part of this is certainly because this was the first time we trained a model on any data that we've collected, but I'm fairly certain that performance would not have been as good if we had chosen what data to label at random."
Angelo Stekardis
"Lightly helped us understand more about our own data-gathering process. Through their service, we were able to see, that a lot of data being collected was not meaningful enough for training an accurate model. This led us to change the way we gathered data and allowed us to ultimately create a much more information-dense and higher-quality dataset overall. Needless to say, the performance of our final model was greatly improved."
Nasib Adriano Naimi
4 easy steps to configure your ML pipeline
Connect
Connect Lightly with your data in GCP, Azure, and AWS S3 buckets. Data stays on your infrastructure, which keeps your it secured
Configure
Use a combination of model predictions, embeddings, and metadata to reach your desired data distribution
Run
Process data on your infrastructure using a docker container. Our solution streams data from the bucket without cluttering disks
Use
Get your curated dataset labeled, train your machine learning model, and check the accuracy improvement
Integrate with your ML Stack
Designed to seamlessly plug into your favorite storage, tooling, and service providers in order to build an automated data pipeline for machine learning that enables a closed loop feedback cycle.
Data Storage
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Label Tooling
Model tooling
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