DataStream: ML Pipeline from Zero to Production
DataStream
2 hours
Deploy Time
12ms
Latency
10x
Experiment Velocity
The Challenge
DataStream had data scientists building models in notebooks with no path to production. Model deployment took weeks and was error-prone.
The Solution
Built a full MLOps platform with automated feature stores, model versioning, A/B testing, and real-time inference serving on Kubernetes.
The Results
Model deployment time reduced from 3 weeks to 2 hours. Inference latency dropped from 200ms to 12ms. Experiment velocity increased 10x.