Standardizing FMCG Operations with the MLOps best practices Across Multiple Cloud Platforms
Challenge
The client faced difficulties in scaling and expanding various ML use cases across multiple cloud platforms. They required a solution to standardize their operations, streamline ML training and inference tasks, and ensure flexibility and scalability across different environments.
Our approach
Our team undertook a transformative project to standardize the client’s ML operations using MLOps templates and best practices. By leveraging cloud technologies and AI, we developed a cloud-agnostic tool and a customized MLOps template that seamlessly integrated ML use cases across GCP, Azure, and Databricks.
Key components of the solution included:
- Automated CI/CD processes aligned with organizational standards.
- Resource autoscaling and consumption logging to optimize cloud resources.
- Model versioning, monitoring, and retraining pipelines, triggered by data or concept drift detection.
- Deployment of a Q&A chatbot powered by ChatGPT and Langchain, offering real-time support for data scientists navigating the tool and templates.
The outcome
The cloud-agnostic tool and standardized MLOps template brought significant improvements to the client’s ML operations. Maintenance tasks were streamlined, reducing overhead costs and increasing operational efficiency. CI/CD pipelines implemented with GitHub Actions accelerated the development cycle, enabling faster transitions from development to production.
Additionally, the integration of a chatbot provided users with real-time support and guidance, boosting user satisfaction and productivity.
Business Impact
The project transformed the client's ML landscape by delivering a standardized, scalable, and efficient solution for training and inference across multiple cloud platforms. This innovation drove cost savings, improved resource utilization, and empowered the client to scale ML use cases more effectively, helping them stay competitive in the dynamic FMCG industry.
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