Portfolio Details

Project Overview:

The customer, an American multinational manufacturing company, sought to build a highly scalable, secure, and cost-effective web application. The company wanted a solution that could efficiently handle dynamic API-driven functionalities while ensuring a seamless user experience. Their core requirements included robust backend services, high availability, and low operational overhead.

Challenges:

The client had developed multiple machine learning models for demand forecasting, customer behavior analysis, and dynamic pricing. While the data science teams were successful in building models in isolated environments, the enterprise faced key challenges:

• Lack of standardized pipelines to deploy and monitor ML models in production.

• Manual handoff between data science and DevOps teams caused delays and operational inefficiencies.

• Difficulty in retraining models using fresh data and scaling across business units.

• No unified observability or governance mechanism to ensure ML model performance in production.

Proposed Solution & Architecture:

Unified Technologies partnered with the client to deliver a production-grade MLOps platform that would bridge the gap between data science and operations. The solution included:

1. Automated Model Deployment Pipelines

• Built end-to-end CI/CD pipelines using GitLab CI and Terraform to automate model packaging, testing, and deployment into AWS SageMaker endpoints and Amazon EKS-based APIs.

• Integrated infrastructure as code to manage SageMaker instances, model artifacts, and endpoint configuration.

2. Feature Store & Data Management

• Implemented a centralized feature store using Amazon S3 and AWS Glue Catalog to standardize feature engineering across teams.

• Ensured data lineage, versioning, and reproducibility of features used in model training.

3. Model Monitoring & Drift Detection

• Integrated CloudWatch and custom Lambda functions for real-time model performance tracking and data drift alerts.

• Used SageMaker Model Monitor to detect bias, latency issues, and stale data in production endpoints.

4. Model Retraining Automation

• Designed a retraining workflow using AWS Step Functions that periodically retrains models based on performance metrics and incoming data.

• Enabled rollback to previous model versions using automated canary deployments and blue/green strategy.

Architecture:

Key Enhancements:

• Reduced ML model deployment time from weeks to under 2 hours.

• Decreased model failure rate in production by 65% through continuous monitoring and observability.

• Enabled self-service model deployment for data scientists without DevOps bottlenecks.

• Improved cross-team collaboration by establishing a single MLOps platform with auditable and reproducible processes.