Sold by: Templefield Technologies
Templefield Technologies transforms validated AI prototypes and MVPs into robust, scalable, production-grade systems on AWS — engineering end-to-end ML pipelines, model serving infrastructure, CI/CD workflows, and operational monitoring. Built on Amazon SageMaker Pipelines, Amazon EKS, AWS Step Functions, and Amazon CloudWatch, every system is designed for reliability, GDPR compliance, and long-term maintainability. Bridges the gap between a promising PoC and a system that delivers consistent business value in production.
Overview
The majority of AI projects fail not at the ideation or PoC stage, but in the transition to production. Templefield Technologies' AI Productionization & MLOps Engineering service exists precisely to close that gap. We take existing PoCs, MVPs, or experimental models and re-engineer them into production-grade AI applications that meet the operational standards of real-world deployments: reliability, performance, security, observability, and regulatory compliance.
Engagement scope ranges from 8 weeks (for well-defined MVP extensions) to 12 months (for complex, multi-component AI systems). Deliverables are tailored to client needs but typically include: robust data pipelines (batch and/or streaming) using AWS Glue and Amazon Kinesis; ML training and retraining workflows via Amazon SageMaker Pipelines; model serving infrastructure using Amazon SageMaker endpoints, AWS Lambda, or containerized workloads on Amazon EKS; CI/CD pipelines for model and application deployment via AWS CodePipeline; and monitoring and alerting for model drift, data quality, and system health using Amazon CloudWatch and AWS Step Functions.
All systems are architected natively on AWS for scalability, cost-efficiency, and auditability — with GDPR, EU AI Act, and enterprise security requirements embedded by design from the first sprint. Clients requiring ongoing support post-delivery can transition into a Managed Services or Retainer engagement. This service is suited for organizations with existing data and cloud infrastructure who are ready to move from AI experimentation to sustainable competitive advantage.
Highlights
- End-to-end MLOps engineering on AWS — Amazon SageMaker Pipelines, Amazon EKS, AWS Step Functions, AWS Glue, and Amazon CloudWatch integrated into production-grade AI systems with CI/CD and automated retraining
- GDPR, EU AI Act, and enterprise security compliance embedded by design — not retrofitted — ensuring production systems are audit-ready and compliant from day one
- Seamless transition to Managed Services or Retainer model post-delivery — Templefield provides ongoing operational support for clients who want continuous AI system management
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Vendor support
Templefield Technologies provides dedicated project and operational support throughout all productionization engagements. Buyers can expect an initial response within 2 business days of inquiry or purchase. Support includes: architecture review call, sprint-based delivery with regular stakeholder updates, code repository and documentation handover, and post-delivery hypercare support. Managed Services and Retainer arrangements available post-delivery.
Contact: daniel.manns@templefieldtech.com