Overview
Artificial Intelligence (AI) has evolved with time to become one of the biggest drivers of technological change. But for the operationalization aspect of AI, there are no silver bullets, Businesses across the spectrum are facing challenges in successfully embedding the AI fabric with the existing applications.
To realize the full potential of AI, there should be a systematic approach of making AI part of mainstream operations, leveraging AI engineering prowess. AI Engineering will help in effectively manage model life cycle. At LTIMindtree, we believe that AI should be part of the mainstream operations process supported by dedicated engineering efforts, which will help in the standardization and streamlining of model life cycle. The AI engineering strategy will facilitate the performance, scalability, interpretability, and reliability of AI models, while delivering the full value of AI investments and improving time- to- market.
Service Offerings
Strategy & Consulting
Enable organizations to re-imagine the business through human and AI interventions.
AI @ Scale
Operationalize AI use cases with an emphasis on end-to-end model management leveraging pre-built utilities.
Governance & Support
Model performance monitoring, drift management, model governance and risk management for an optimum value realization.
AI/ML- Specific Testing
Assure the adoption of AI models by having a holistic AI/ML testing framework for Data, Model and AI/ML infrastructure testing.
Value Adds
AInA Model Ops Templates
M-AINA-Model Monitoring App
AI Engineering templates for Customer Analytics
Model Testing Framework
AaRT Framework
