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> ML_ATLAS // REFERENCE_RECIPES_v2.1

Production Reference Recipes

Battle-tested architectural recipes connecting analytical tasks, heuristic baselines, hardware topologies, and regulatory safeguards.

8Reference Recipes
15Regulated Sectors
3-TierPlacement Tiers
100%Permissive / Qualified
binary classificationsaas ecommerce

SaaS Subscription Churn Prediction

Identify accounts at high risk of cancelling 60 days before contract renewal, enabling proactive CSM intervention.

⚡ Heuristic Baseline:

Heuristic rule: Flag any account where primary admin login has been inactive for > 30 days.

Training: Scheduled batch pipeline on CPU server (e.g. 4 cores, 16GB RAM)
Inference: Scheduled weekly batch job writing churn probabilities back to CRM
semantic search ragsaas ecommerce

Enterprise Technical Documentation RAG

Empower engineers and customer support agents to query internal runbooks and architecture docs with cited, verifiable answers.

⚡ Heuristic Baseline:

Full-text keyword search (Elasticsearch / PostgreSQL BM25) returning top matching document snippets.

Training: Pretrained off-the-shelf models; zero custom model training required
Inference: Embedding generation on CPU server; LLM synthesis via vLLM server GPU or secure hosted API
anomaly detectionmanufacturing

Industrial IoT Predictive Maintenance & Anomaly Detection

Detect early bearing wear and vibration anomalies on factory turbines 72 hours before catastrophic thermal seizure.

⚡ Heuristic Baseline:

Static threshold alarm: Trigger alert when peak vibration amplitude exceeds 4.5 mm/s RMS.

Training: Trained quarterly on server CPU using verified healthy operating runs
Inference: Edge gateway IPC running ONNX Runtime on industrial low-power CPU
binary classificationfinance

Real-Time Financial Transaction Fraud Detection

Evaluate card payments and wire transactions for fraud probability under a target SLA of sub-10ms p99 latency, preventing financial loss while minimizing false declines.

⚡ Heuristic Baseline:

Hardcoded rule engine: Flag transactions with amount > $5,000 or country mismatch between cardholder and merchant IP.

Training: Scheduled daily retraining on CPU/GPU server cluster with temporal cross-validation
Inference: Stateless auto-scaling CPU microservice co-located with transaction payment gateway
time series forecastingretail cpg

Hierarchical Retail Store-SKU Demand Forecasting

Forecast 14-day daily unit demand for 50,000 store-SKU combinations to optimize inventory replenishment and minimize stockouts.

⚡ Heuristic Baseline:

Seasonal Moving Average: 4-week historical sales mean for the same day of the week.

Training: Scheduled weekly batch distributed training job on CPU cluster (e.g. 16 cores, 64GB RAM)
Inference: Scheduled daily batch job generating 14-day rolling purchase recommendations
object detectionmanufacturing

Automated Optical Inspection (AOI) for Surface Defects

Segment and classify microscopic scratches, dents, and voids on manufactured PCB and metallic components at 60 parts per minute.

⚡ Heuristic Baseline:

Computer vision morphological filter: Canny edge detector + contour thresholding flagging surface blobs.

Training: Quarterly training on developer/cloud GPU workstation (e.g. 1x RTX 4090 or A10G)
Inference: Industrial edge appliance (NVIDIA Jetson Orin or industrial IPC with TensorRT)
feature extractionhealthcare

Clinical Notes NER & HIPAA/KVKK De-identification

Extract medical conditions, dosages, and protect patient privacy by scrubbing names, dates, and locations from unstructured doctor notes with 99.5% entity recall under deterministic human supervision.

⚡ Heuristic Baseline:

Regex pattern matching: Comprehensive regular expressions scanning for SSN, phone numbers, Turkish TC Kimlik, and date formats.

Training: Fine-tuned on isolated private GPU server with synthetic/de-identified training data
Inference: On-premise air-gapped hospital server running CPU inference under private intranet
recommender systemsretail cpg

Real-Time E-Commerce Session & Next-Item Recommendation

Recommend personalized related products based on in-session click sequences in < 15ms, boosting cart additions and cross-sell revenue while maintaining strict user privacy boundaries.

⚡ Heuristic Baseline:

Category bestsellers: Display top 10 most purchased items in the currently viewed category over the past 7 days.

Training: Nightly batch training job on multi-core CPU or single GPU server
Inference: In-memory vector lookup microservice (Redis cache) running on API server CPU