Data Engineering & MLOps
We build data pipelines and MLOps foundations — monitoring, retraining plans, and deployment patterns — so AI works reliably in real environments.
ArchitectureBuildDeployMonitorImprove
Deliverables
What You Get
01
Data pipeline architecture02
Data quality checks baseline03
Model deployment pattern04
Monitoring (drift/latency/cost)05
Retraining plan + triggers06
Documentation + runbookFeatures
Key Features
01
ETL/ELT pipelines02
Data quality validation03
Model monitoring04
Drift detection05
Retraining workflows06
Cost and latency optimization07
Scalable deployment08
Versioning basics09
Reliability patterns10
Secure AI integrationsWorkflow
Our Process
01
Architecture
Design data pipeline and MLOps infrastructure.
02
Build
Implement ETL pipelines and quality checks.
03
Deploy
Set up model serving and deployment patterns.
04
Monitor
Configure drift detection, latency, and cost monitoring.
05
Improve
Iterate on pipelines and models based on production data.
Ideal For
AI products, high-traffic chatbots, predictive systems.
Checklist
What We Need From You
To get started, please prepare the following:
AI model(s) to deploy
Data sources and formats
Performance requirements
Infrastructure access
Monitoring preferences
Portfolio
