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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.
القدرات
هندسة المنتجات
الذكاء الاصطناعي والبيانات والأنظمة الذكية
تجربة المستخدم وتصميم المنتجات
هندسة التجربة
الأمن السيبراني
هندسة الامتثال
الصناعات
الطيران
الخدمات المصرفية
الخدمات المالية والتأمين
التعليم
الرعاية الصحية وعلوم الحياة
الاتصالات
إنترنت الأشياء الصناعي
تصميم وتصنيع الإلكترونيات
نمذجة الذكاء الاصطناعي
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