Data and Annotation Quality

Almost all computer vision experts trying to deploy supervised learning solutions are forced into an impasse. The inevitable 80/20 rule leaves data scientists and ML engineers spending only 20% of their time on data analysis or fine-tuning algorithms. The other 80% of their time is focused on preparing usable data. The majority of data generated today is unstructured and requires humans-in-the-loop to become useful.  This remains true for supervised learning models in the computer vision space. To correctly identify the objects of interest, scientists and engineers require accurately labeled datasets for training and validation. But the challenge of data aggregation, cleaning, and annotation is physically and mentally exhausting. Machine learning engineers should be spending their valuable time on algorithm development, feature engineering, and model tuning instead of scrutinizing datasets to ensure data and annotation quality.

Our Roots

At Innotescus, we are dedicated to enabling scientists and engineers to shift their focus from structuring and curating datasets to the more impactful part of machine learning, algorithm development. Our roots lie in developing software solutions using AI and ML integrated with the imaging systems developed by our parent organization, ChemImage Corporation.  We understand the pain points of having to construct a quality training and validation dataset before focusing on the solution. Coming from this metric and data-driven culture, we appreciate the need for quality data, labeled data, deep insights into data, and the tremendous impact these factors can have on solution performance.

Shifting Focus

To help scientists and engineers focus on their supervised learning solution and spend less time managing and annotating data, we developed smart tools in a scalable cloud platform. With these smart tools and exploratory data analysis features, we enable users to create better training datasets, faster. Scientists and engineers can use our platform to reduce the amount of time they spend on data wrangling and reallocate that time to perfecting the models. Not only will our platform reduce the time of data annotation, but it also increases data quality through a human-in-the-loop approach.  The exploratory data analysis results will provide experts with deeper insights into the inherent biases present in the training dataset to avoid the “black box” approach and reduce the interpretability problem on developing ML solutions to further reduce wasted time and resources. Performance, accuracy, and usability of the Innotescus platform will increase speed to market and performance for our clients’ supervised learning solutions.

Seeking Computer Vision Professionals

We are seeking computer vision professionals that are frustrated with the quality and amount of time spent managing data to become early adopters for our comprehensive solution by participating in our Pilot Program.

Apply to be considered for participation in our Pilot Program

Free EBook - resolve 5 common ML Data Cleaning Problems