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 Google Cloud data platform.
To realize the full potential of AI, LTM 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.
LTM Approach to AI Engineering on Google Cloud
Our Google Cloud 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 Google Cloud data platform.
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 Google Cloud 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
- End-to-end reusable model operationalization templates.
- Migration templates to Google Cloud vertex AI.
Benefit: Quick and scalable integration with the data platform for holistic ModelOps.
- 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.
- 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.
- AI/ML model testing framework with in-built utilities
- Easy integration with key Google Cloud components (Vertex AI, Big Query)
Benefit: Revitalize the model lifecycle and feedback mechanism across models on Google Cloud.
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