Business Challenge and Goals

Business Challenge

Developing Advanced Driver-Assistance Systems (ADAS) for trucks and cars requires not just accurate models, but a production-grade Data Engine capable of continuously ingesting, curating, and learning from massive multi-modal sensor data.

• Scale vs. Resources: Each vehicle could generate 20–40 TB of data per day, creating petabyte-scale challenges—but the team had to solve this with a small engineering staff and startup-level budgets.

• Safety-Critical Domain: Unlike e-commerce or IoT analytics, even a single misclassification in ADAS could result in real-world accidents. This demanded 99.9%+ reliability across diverse conditions.

• Long-Tail Edge Cases: The majority of raw driving logs contained uninteresting data, but <1% of scenarios (e.g., emergency lane changes, night-time cut-ins, occluded pedestrians) were critical for safety and generalization.

• Operationalization Gap: Models could not remain research artifacts. They had to be productionized with CI/CD, monitoring, retraining, and governance in line with MLOps best practices.

The company needed a data-centric MLOps solution that could close the loop: Collect → Curate → Label → Train → Deploy → Monitor → Retrain.

---

Goals

The project’s overarching goals were to:

1. Architect a Production-Grade ADAS Data Engine on AWS for cars and trucks, enabling scalable ingestion, curation, labeling, training, and deployment.

2. Enable Continuous Improvement of perception and inference models via a closed-loop system inspired by Tesla’s “Operation Vacation” data engine.

3. Operationalize MLOps Best Practices for a small, cross-functional startup team (Product Manager, Data Engineer, ML/MLOps Engineer).

4. Balance Cost, Latency, and Reliability — optimizing AWS cloud pipelines for performance while staying within realistic startup cost constraints.

---

Primary Business KPIs

These metrics directly measured business value and safety outcomes:

KPI	Description	Target Outcome
Reduction in False Positives/Negatives	% reduction in critical perception model errors (e.g., misclassified vehicles, missed pedestrians).	20–25% reduction after full pipeline deployment.
ADAS Feature Reliability	Frequency of disengagements or system overrides in assisted driving.	15–20% fewer disengagements in fleet tests.
Time-to-Model-Update (TTMU)	Time from discovering a new failure mode to deploying an updated model.	Reduced from 8–10 weeks → 2–3 weeks.
Fleet Safety Improvement	Incidents avoided due to perception/ADAS alerts.	Internal validation: ~22% reduction in safety-critical failures across test drives.

---

Secondary Engagement KPIs

These tracked engineering efficiency and organizational maturity:

KPI	Description	Target Outcome
Data Pipeline Latency	Time from raw ingestion → curated dataset availability.	Under 24 hours per drive log.
Model Training Throughput	Number of experiments completed per week.	Increase from ~2/week → ~8–10/week.
CI/CD Automation Coverage	% of workflows (data, model, infra) automated via GitHub Actions + Terraform.	>85% automated.
Data Governance Compliance	Traceability of dataset → model → deployment (ISO 26262 readiness).	Full lineage tracked in MLflow + DVC.
Cross-Functional Iteration Speed	Average cycle time between ML, data engineering, and product validation.	Reduced by 40% through shared pipelines.

---