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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 LTI, 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

 
LTI AInA

LTI AInA

Template based approach to Operationalize AI/ML Models @Scale & @Pace

LTI M-AINA

LTI M-AINA

Unified AI/ML model monitoring application to invigorate the model performance & business value

AInA Model Ops Templates

AInA Model Ops Templates

  • End-to-end reusable model operationalization templates
  • Easy integration with Azure, AWS, GCP & Snowflake platforms

Benefit: Quick & scalable integration along the data platform for holistic Model Ops.

M-AINA-Model Monitoring App

M-AINA-Model Monitoring App

  • Centralized views of all the models in production
  • Key metrics in a snapshot for-model health, data drift and service health

Benefit: Model monitoring across key parameters with ease.

AI Engineering templates for Customer Analytics

AI Engineering templates for Customer Analytics

  • End-to-end customer analytics templates using Model Ops
  • In-built statistical models having Segmentation, Churn, CLTV, Cross Sell, etc.

Benefit: Pre-built utility to fast-track the value realization across customer analytics.

Model Testing Framework

Model Testing Framework

  • AI/ML model testing framework with in-built utilities
  • Easy integration with Azure, AWS, GCP and Snowflake data platforms

Benefit: Invigorate the model lifecycle and feedback mechanism across models.

AaRT Framework

AaRT Framework

  • Holistic AI engineering framework model
  • In-built templates to operationalize, monitor, govern & test

Benefit: End-to-end framework having holistic AI strategy @Scale

Refract

Refract

  • Comprehensive no-code/low-code AI platform
  • In-built feature for operationalization of ML models @ scale

Benefit: Product experience and expertise to jumpstart your AI journey.

Case Studies

Learn how LTI helps organizations across the world innovate with AI Engineering

 
Enterprise AI Strategy

Enterprise AI Strategy

Global FMCG major partners for holistic model monitoring strategy

Invigorating AI Models

Invigorating AI Models

US based utilities major achieves scalability & time to market with AI engineering

ModelOps transformation

ModelOps transformation

Europe based elevator major transforms its customer experience

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