Nia mitigates these challenges and assures fastest time-to-value for your AI implementation.
Nia Data provides highly effective tools and frameworks for complex data workflows to power further ML experimentation on the Nia AML workbench.
Machine learning workbench and toolkit for experts and citizen data scientists to simplify creation and explainability of AI Models.
Nia Deep Learning with unsupervised feature learning and deep learning capabilities can automatically learn feature representations from unlabeled data.
Nia Model Ops integrates seamlessly with key Nia components to deploy, orchestrate and monitor models in either in-house or cloud base deployments.
Nia Vision offers a set of APIs for the analysis of images and documents by leveraging Artificial Intelligence (AI) and the best-of-breed computer vision technologies.
Nia NLP capabilities allows extraction of useful insights from raw text and lends itself to a variety of applications to enable further processing in applications and business logic.
Nia Knowledge capability helps enterprises to organize information into an ontology-based knowledge base so that additional knowledge can be inferred and queried.
Nia Cognitive Search provides enterprises with the capability to retrieve precise answers from diverse enterprise data sources and formats through the effective use of ML and Neural Models.
Toolkit for development of chatbots to deliver contextual and conversational experience across channels of preference.
Infosys Nia Chatbot brings the power of conversational artificial intelligence to build smart conversational user interfaces on core business systems, existing enterprise channels
Explainable AI helps industries benefit significantly by providing a deeper understanding of data, uncovering any bias in data, and further improving models and explaining how decisions are made.
Artificial Intelligence (AI) is invariably transforming business operations and making existing enterprise operation models more efficient. But the real value of AI lies in solving complex problems.
According to the International Institute for Analytics, only 15% of organizations deploy their ML models in production successfully. The reasons are — (a) Most enterprises are using AI for automatin