Centralized GCP MLOps Platform for Cost Efficiency and Streamlined Development

The client faced the challenge of developing, deploying, and maintaining multiple ML production-ready use cases across different cloud platforms, leading to inefficiencies in resource consumption and maintenance. They sought a solution that would centralize their ML operations onto a single, scalable platform while streamlining their development and deployment processes through effective CI/CD.

Our team embarked on a comprehensive project to centralize the client’s diverse use cases onto GCP. Leveraging the power of GCP’s robust infrastructure and services, we designed and implemented a flexible and scalable solution that accommodated various business operational needs.

A single production platform which automatically tests, integrates, and deploys new ML algorithms in production as well as model monitoring and retraining.

Key implementations:

• Automated process of CI/CD that meets organization’s standards and best practices.
• Auto-ticketing for the operations team when ML pipelines fail (e-mails and tickets).
• Resource autoscaling and consumptions logging.
• Model versioning, model monitoring, and model retraining pipelines (triggered if data / concept drift is detected).

Through strategic utilization of GCP and innovative technologies like Docker, Kubernetes, and CI/CD, we successfully improved and standardized our client’s process of developing, training, productionizing, and maintaining ML models. We delivered a centralized, cost-effective, and agile platform for their business operations. This project stands as a testament to our expertise in MLOps and our commitment to driving innovation and efficiency for our clients in the fast-paced world of FMCG.

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