Artificial Intelligence, or simply AI, has evolved today to become a central driver and enabler of technological evolution. Google Cloud Platform (GCP) has helped leading global organizations solve the most demanding business challenges with AI-powered engines by leveraging innovative machine learning products and services. However, there are no silver bullets for AI operationalization. Businesses across the spectrum face an uphill task to utilize and monitor their AI/ML models on the GCP data platform.

To realize the full potential of AI, LTI experts work with the principle that artificial intelligence should be a core element of the mainstream operations process supported by dedicated engineering efforts. This is vital to standardize and streamline the model life cycle. A robust AI engineering strategy facilitates AI models’ performance, scalability, interpretability, and reliability while delivering the total value of investments and ensuring faster time-to-market.

LTI’s Approach to AI Engineering on GCP

Our GCP AI engineering team helps drive the correct application of AI models by embedding the AI fabric with existing applications and extracting comprehensive value on the AI investments on GCP. Our AaRT framework, backed by its in-built templates to operationalize, monitor, govern, and test, delivers AI projects beyond proofs of concept and prototypes to full-scale production by leveraging the GCP data platform

AaRT Framework Model for AI engineering on GCP

AaRT Framework


Operationalize AI use cases with an emphasis on end-to-end model management using Vertex AI.

  • Solve for Use Cases: Enterprise AI solutions for solving business problems
  • AI Engineering @ Scale: Model deployment services across disparate frameworks and platforms
  • Scalable and Continuous AI/ML solutions: Re-usable ModelOps template for model operationalization and management across cloud platforms with CI/CD/CT/CM.

Model performance monitoring and AI-specific model testing for holistic governance with optimum value realization.

  • AI/ML Specific Testing: Data, Model & AI/ML infrastructure testing
  • Model Management and Governance: Ethical and responsible AI complying with regulations
  • Model Monitoring: Establish a feedback loop mechanism by monitoring drift, service health, and ground truth evaluation.

Re-imagine the business through human and AI-powered lens by having a holistic ModelOps strategy.

  • Strategical Staking: Re-invent business with a successful AI adoption leveraging ModelOps
  • AI Maturity Assessment: Assessing the maturity of the organizations in implementing and operationalizing ML
  • AI Roadmap: Identify opportunities to leverage AI/ML across the business.

Build a future-ready AI platform on GCP powered by AI engineering capabilities.

  • Architect for Future: Harness the power of AI by building capabilities to transform business
  • Invigorate Adoption: Help businesses adopt technology by delivering value faster and reducing time-to-market.
  • Value Realization: Measure execution, experience, and impact on business.

Key Value Assets

Invigorate your AI journey on GCP by leveraging LTI’s pre-built AI engineering utilities, including.

AInA Model Ops Template

  • End-to-end reusable model operationalization templates
  • Migration templates to GCP vertex AI

Benefit: Quick and scalable integration with the data platform for holistic ModelOps.

M-AINA: Model Monitoring App

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

Benefit: Easy and simplified model monitoring across key parameters.

AI Engineering Templates for Customer Analytics

  • End-to-end customer analytics templates using ModelOps on Vertex AI
  • In-built statistical models having segmentation, churn, CLTV, cross-sell, and more

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

Model Testing Framework

  • AI/ML model testing framework with in-built utilities
  • Easy integration with key GCP components (Vertex AI, Big Query)

Benefit: Revitalize the model lifecycle and feedback mechanism across models on GCP.