Real-Time Analytics and ML-Driven DevOps for Smarter Fleet Logistics

Manual Infrastructure Management Slows Innovation: Many automotive parts companies still rely on manual or semi-automated cloud infrastructure setups, which limit scalability, delay provisioning, and increase the risk of misconfigurations in high-compliance environments.

Lack of Streamlined Deployment for Machine Learning Models: As AI and predictive analytics become more embedded in operations (e.g., demand forecasting, defect detection), companies struggle to consistently deploy and manage ML models across environments, hindering speed, experimentation, and impact.

Insufficient Monitoring and Incident Response for AI/ML Services: Small failures can go undetected without centralized monitoring and real-time alerting for ML pipelines and infrastructure, compromising system reliability and delaying response in high-throughput production settings.

Ensure scalability, automation, and reliability throughout the entire ML lifecycle.

A digital banking app was developed through close collaboration between frontend, backend, and QA teams.

Infrastructure as Code and Automation: Use of Terraform to automate and manage cloud infrastructure, ensuring secure and on-demand access to services such as S3, RDS, and Kinesis.

CI/CD for ML Pipelines: Implementation of Jenkins libraries and pipeline templates to help data scientists and ML engineers easily deploy and manage models across different environments (Development, Testing, Production).

Monitoring and Reliability: Monitoring ML infrastructure via CloudWatch (formerly DataDog), with alerts sent through Teams, enabling quick incident response and ensuring ML services run smoothly.

Release Management and Governance: Adoption of GitFlow and ML-Ops best practices, including structured releases, version control, PR validation, to maintain high deployment standards. We follow ML-Ops best practices, such as:

• Automated model deployment

• A/B Testing for automated rollouts

• Model troubleshooting and monitoring

Our DevOps environment supports an ML ecosystem primarily based on Python, using:

• AWS SageMaker, Lambda, and Redshift for model training and execution

• SNS/SQS for message exchange between ML components

• Streamlit for publishing ML insights and results