CASE STUDY · ML INFRASTRUCTURE
Flight-Test MLOps and Data Pipeline
Turning fragmented test imagery into a repeatable, traceable ML data path for faster preprocessing, training, deployment, and engineering analysis.
Problem
Machine-learning iteration slows dramatically when test data is difficult to discover, decode, preprocess, reproduce, and connect to training runs. For flight-test imagery and ML systems, the data infrastructure can become the limiting reagent for model development and downstream automation.
Constraints
The pipeline had to support large test datasets, repeatable preprocessing, model-development workflows, and engineering analysis while fitting existing organizational data practices.
Approach
I designed and implemented an ETL-based MLOps pipeline for flight-test imagery and also designed the first centralized flight-test database used to support pre-flight and post-flight missile-system evaluations.
The architecture separates the major responsibilities:
raw test data → validation → transformation → indexed storage → training/evaluation datasets → model workflow
This separation makes data lineage easier to reason about and allows expensive transformations to be cached or parallelized rather than repeated manually for every experiment. It also establishes a dependable interface between test data, analysis tools, and the engineers who need to act on results.
Results
The pipeline accelerated preprocessing for model training and deployment. The centralized database streamlined data retrieval and analysis, enabling faster performance assessments.
Engineering notes
MLOps for embedded autonomy is not only experiment tracking. The valuable pipeline connects test evidence, preprocessing code, model inputs, deployment artifacts, and system-level evaluation so an engineer can answer not just “which model is best?” but “which data and transformations produced the behavior we are seeing?” That traceability is what makes automation useful in an engineering environment.