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Case Study
An End-to-End Model Preparation and Validation Pipeline
The client got a repeatable, measurable path from data to a validated, deployable model, with the accuracy evidence to back it.
Industry
AI, Data & MLOps
Services We offer
MLOps
Computer Vision
Model Optimization
CI/CD
About the client
A global medical-technology company.
Problem Statement
The client needed to reliably create, train and validate models that detect annotated elements in images, and to prove the results, with an end-to-end pipeline rather than a one-off notebook.
The Challenges
Repeatable, not one-off
Wiring training and validation into CI made model preparation a measurable, auditable process.
Optimized for the target
Converting to OpenVINO IR kept inference efficient for both real-time and batch use.
Approach
We built the full lifecycle:
- Dataset creation with Intel CVAT annotation
- Model training in TensorFlow
- Optimization to OpenVINO IR format for efficient inference
- Real-time and batch inference paths
- Validation against comparable datasets, wired into CI
The Solution
Data flows from annotation through training to an optimized IR model, then into both real-time and batch inference, with validation and report generation on every run through a CI pipeline and Git-based operations.
Get Detailed Case Study
An End-to-End Model Preparation and Validation Pipeline
A pipeline to create, train, optimize and validate computer-vision models for detecting annotated elements in images, with real-time and batch inference and automated reporting.