MLOps Pipeline Automation
Impact65-75% efficiency boost
Deployment Time-75%
Failed Deployments0
Cycle TimeWeeks → Days
Overview
Redesigned the MLOps infrastructure for a food delivery unicorn to improve model deployment velocity and reliability. The project involved auditing existing workflows, identifying bottlenecks, and implementing modern MLOps practices.
The Challenge
The data science team was struggling with slow model deployment cycles (weeks to production), unreliable pipelines, and lack of monitoring.
The Solution
Implemented a comprehensive MLOps framework using AWS services including SageMaker, Step Functions, and CodePipeline, with automated testing, monitoring, and rollback capabilities.
Key Results
65-75% reduction in deployment time
Model deployment cycle reduced from weeks to days
Zero failed deployments in 6 months post-implementation
Tech Stack
AWS SageMakerStep FunctionsCodePipelineLambdaCloudWatchDockerKubernetes