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Our Process
We follow a structured yet flexible approach that aligns technology with your business goals. From in-depth discovery to seamless execution, every step is designed to deliver measurable impact. Our experts ensure transparency, collaboration, and innovation throughout the journey.
1
Current State Assessment
Map existing CI CD pipelines, branching strategy and model lifecycle maturity.
2
Strategy and Tooling Selection
Choose the right orchestration, testing and monitoring stack for scale and governance.
3
Automation and IaC Implementation
Implement pipelines, feature toggles, IaC modules and automated tests.
4
Model Training to Serve Pipeline
Automate training, validation, deployment and rollbacks for models.
5
Monitoring and Continuous Improvement
Set SLOs, observability and auto remediations for anomalies and regressions.
Success Story
Real results from our enterprise implementations.
Releases were risky and ML models drifted quickly leading to wrong recommendations and lost revenue.
The Challenge
The client struggled with unreliable model deployments and limited visibility into production performance. Release cycles were slow, and retraining only happened manually, leading to degraded model accuracy over time.
Our Approach
We introduced a structured MLOps workflow that improved deployment reliability and monitoring. This included automated CI/CD for model releases, controlled rollout strategies, and a monitoring framework that identified performance drift and triggered retraining.
End-to-end CI/CD implementation
Automated canary releases
Model monitoring framework
Impact & Results
5x
Release frequency increase
20%
Recommendation revenue increase
Automated
Model drift detection
Tech Stack
Databricks
MLflow
Pyspark
"Before partnering with Inpro Analytics, releasing ML models into production was risky and we had limited visibility into performance. Models drifted quickly, recommendations degraded, and revenue was impacted. Inpro implemented a structured MLOps workflow with end to end CI CD, controlled rollout strategies including canary releases, and monitoring that automatically detected drift and triggered retraining. The change was immediate: our release frequency increased fivefold, model quality stayed stable over time, and recommendation driven revenue rose by 20%. We now ship confidently with clear controls and reliable performance in production."
— Client Representative