We develop practical data-driven algorithms to build machine learning solutions for businesses by classifying the AI hype from computational realities.
Building a most fitting solution involving machine learning is the art of understanding complex mix of structured and unstructured data model training, model integration, and architecture. We undergo in-depth research to offer end-to-end machine learning solutions, having features leading to better results.
In this growing competitive world, there are a plethora of companies offering NLP APIs and services. Some of these services provide 80% accuracy on extraction tasks involving generic data but fails to solve complex challenges like natural language understanding, especially with proprietary and small data sets. Juppiter AI Labs, with its highly professional tech team, potentially uses machine learning techniques along with traditional NLP algorithms.
Deep learning techniques have changed the outlook of computer vision and image processing solutions. Yet, training models for proprietary and domain-specific data sets remain a challenge. Juppiter AI Labs helps by using innovative ways to change the domain-specific part of a problem into a generic computational problem to offer practical and optimal solutions.
We all know that optimization algorithms are the soul of modern-day machine learning. At Juppiter, we potentially employ these fundamental algorithms to churn the aptest solutions to resolve allocation, routing, and balancing issues.