🐙 Marc Mengler

Better datasets create better machine learning models, enabling better products.Quality Match verifies and improves the quality of datasets for computer vision and machine learning. This can be applied to: - dataset architecture (are my dataset and taxonomy good?) - measure the quality of annotation providers (is the quality as advertised?) - evaluate model performance (when does the model fail?) - supervise model-drift (is the model still working?) and many more use cases. We focus on creating actionable, quantitative metrics on dataset quality, such as: - representativeness (bias analysis) - accuracy (labels, detection rates and geometric properties of annotations) - and ambiguity (edge cases due to bad data or bad taxonomies). We do this by breaking down quality questions into a large decision tree where each node is either a training-free, unambiguous crowd-sourcing task which is repeated until statistical significance, or a pretrained machine learning model with known confusion matrix. We call this system our Annotation Quality Engine. To verify your data, we help you verifying and integrating your metrics into our Annotation Quality Engine which you will then be able to query through a simple REST API.

We are a collection of engineers and greenhouse industry veterans on a mission to secure the world's food supply by making greenhouse operations reliable, systematic, and scalable. High-tech greenhouses are key to our agricultural future - for improving human nutrition and for securing our food system against climate change and population growth. We are doing everything we can to accelerate the deployment of new farms around the world. Find out more about our mission in this 2 min video: youtu.be/LDJ_QdUaap4. We are backed by leading deep technology VC funds.

We want to unlock the world’s data by building the global data graph. By making the global data graph a reality, Stellate unlocks the compounding effects of innovation by removing the inefficiencies of connecting poorly structured and documented APIs. The recaptured development time alone is significant — less time spent on busy work allows for more time spent on innovation and value creation.

AI-powered testing tool for web apps that finds bugs before your users do. We only need your website’s URL. Our AI agent knows what to test, writes the tests and keeps them relevant. Run the tests from our app or plug them into your CI/CD pipeline. Beta out now 🟢

Better datasets create better machine learning models, enabling better products.Quality Match verifies and improves the quality of datasets for computer vision and machine learning. This can be applied to: - dataset architecture (are my dataset and taxonomy good?) - measure the quality of annotation providers (is the quality as advertised?) - evaluate model performance (when does the model fail?) - supervise model-drift (is the model still working?) and many more use cases. We focus on creating actionable, quantitative metrics on dataset quality, such as: - representativeness (bias analysis) - accuracy (labels, detection rates and geometric properties of annotations) - and ambiguity (edge cases due to bad data or bad taxonomies). We do this by breaking down quality questions into a large decision tree where each node is either a training-free, unambiguous crowd-sourcing task which is repeated until statistical significance, or a pretrained machine learning model with known confusion matrix. We call this system our Annotation Quality Engine. To verify your data, we help you verifying and integrating your metrics into our Annotation Quality Engine which you will then be able to query through a simple REST API.

We are a collection of engineers and greenhouse industry veterans on a mission to secure the world's food supply by making greenhouse operations reliable, systematic, and scalable. High-tech greenhouses are key to our agricultural future - for improving human nutrition and for securing our food system against climate change and population growth. We are doing everything we can to accelerate the deployment of new farms around the world. Find out more about our mission in this 2 min video: youtu.be/LDJ_QdUaap4. We are backed by leading deep technology VC funds.

We want to unlock the world’s data by building the global data graph. By making the global data graph a reality, Stellate unlocks the compounding effects of innovation by removing the inefficiencies of connecting poorly structured and documented APIs. The recaptured development time alone is significant — less time spent on busy work allows for more time spent on innovation and value creation.

We believe that AI is the most powerful tool our generation has at hand. At understand.ai, we make deep learning accessible for real-world applications like self-driving cars. Weve created algorithmic annotation tooling for camera, lidar and sensor fusion data. By introducing the automation engine for data annotation to UAI Annotator – our annotation tool, we deliver unprecedented automation rates cutting costs and accelerating the training and testing of assisted & autonomous driving technology. We are AI natives data annotation experts with 6 years of worldwide AD/ ADAS project experience and 80+ associates with AI, automotive & cloud background. We are your annotation partner for your autonomous journey.