We engineer practical data-driven algorithms to implement machine learning solutions for startups by separating the AI hype from computational realities.
Building a solution involving machine learning is much more than the model. It is a complex mix of data structures, model training, model integration and architecture. We engage in end-to-end delivery of a machine learning solution tailored to bring product features to life.
There are many NLP APIs and services available today. Some of these services could give 80% accuracy on extraction tasks involving generic data. However, to solve really hard problems involving natural language understanding, especially with proprietary and small data sets, we need to skillfully use machine learning techniques along with traditional NLP algorithms.
Deep learning techniques have given a fillip to computer vision and image processing solutions. However, training models for proprietary and domain-specific data sets is a challenge. We find innovative ways to transform the domain-specific part of a problem into a generic computational problem in order to deliver practical solutions.
Classifying cell structures and recognizing similar regions in tissue samples.
Modeling machine breakdown using supervised learning over high dimensional time-series data
Identifying and extracting key concepts, questions in chat conversations/ reviews and recognizing values for domain attributes.
Classifying cell structures and recognizing similar regions in tissue samples.
Modeling machine breakdown using supervised learning over high dimensional time-series data
Identifying and extracting key concepts, questions in chat conversations/ reviews and recognizing values for domain attributes.