ML Engineering Projects - Detailed Documentation

Real-Time Personalization Engine

High-performance recommendation system with sub-100ms latency

Overview

This personalization engine processes user events in real-time to generate personalized recommendations. Built for scale, it handles 50K+ requests per second while maintaining sub-100ms response times.

Key Features

Real-time event processing with Kafka streaming
Redis-based caching for ultra-low latency
XGBoost models for recommendation scoring
A/B testing framework for continuous optimization
Comprehensive monitoring and alerting

Architecture

Sample API Usage

# Get personalized recommendations
curl -X POST https://nicholstechconsulting.com/api/events \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "user_123",
    "event_type": "page_view",
    "context": {"page": "product_detail", "product_id": "prod_456"}
  }'

# Response:
{
  "recommendations": [
    {"product_id": "prod_789", "score": 0.92},
    {"product_id": "prod_012", "score": 0.87}
  ],
  "latency_ms": 23
}
            

View Live API API Documentation

MLOps Pipeline & CI/CD

Automated ML lifecycle management with GitHub Actions

Overview

Comprehensive MLOps pipeline that automates the entire ML lifecycle from data validation to model deployment. Features automated testing, versioning, and blue-green deployments.

Pipeline Stages

Data Validation - Automated data quality checks and drift detection
Model Training - Distributed training with hyperparameter optimization
Model Validation - Comprehensive testing including A/B tests
Deployment - Blue-green deployment with automatic rollback
Monitoring - Real-time performance tracking and alerting

CI/CD Configuration

name: ML Pipeline
on:
  push:
    branches: [main]
  schedule:
    - cron: '0 0 * * *'  # Daily retraining

jobs:
  validate-data:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Validate Data Quality
        run: python scripts/validate_data.py
        
  train-model:
    needs: validate-data
    runs-on: ubuntu-latest
    steps:
      - name: Train Model
        run: python scripts/train_model.py
      - name: Evaluate Model
        run: python scripts/evaluate_model.py
            

View Architecture

Scalable Feature Store

Enterprise-grade feature management system

Overview

Centralized feature store that manages features for all ML models. Provides consistent feature computation, versioning, and lineage tracking with both batch and real-time serving capabilities.

Key Capabilities

Dual storage architecture (PostgreSQL for batch, Redis for real-time)
Feature versioning and lineage tracking
Point-in-time correct feature retrieval
Feature monitoring and data quality checks
SDK for Python, Java, and Go

Feature Definition Example

# Define a new feature
from feature_store import Feature, FeatureGroup

user_features = FeatureGroup(
    name="user_engagement",
    features=[
        Feature(
            name="avg_session_duration",
            dtype="float",
            transformation="avg(session_duration) over 7d"
        ),
        Feature(
            name="purchase_frequency",
            dtype="int",
            transformation="count(purchases) over 30d"
        )
    ]
)

# Register features
feature_store.register(user_features)

# Retrieve features
features = feature_store.get_features(
    entity_ids=["user_123", "user_456"],
    feature_names=["avg_session_duration", "purchase_frequency"]
)
            

View Live API API Documentation

Interactive ML Dashboard

Comprehensive monitoring and analytics platform

Overview

Full-featured dashboard for monitoring ML models in production. Provides real-time metrics, performance analytics, and business intelligence insights.

Dashboard Features

Model Explorer - Compare model versions and performance
Feature Analytics - Feature importance and drift monitoring
Performance Monitoring - Real-time metrics and alerts
Business Insights - ROI analysis and impact metrics

Technologies Used

Streamlit for interactive UI
Plotly for advanced visualizations
Prometheus for metrics collection
Real-time WebSocket updates

Launch Dashboard

Dynamic Rules Engine

High-performance business rules execution system

Overview

Flexible rules engine that allows business users to define and modify rules without code changes. Built with Go for maximum performance, it evaluates complex rule sets in under 1ms.

Features

DSL for rule definition
Real-time rule updates via etcd
A/B testing framework
Comprehensive rule analytics
gRPC API for high throughput

Rule Definition Example

rule "high_value_customer_discount"
when
    customer.lifetime_value > 1000 AND
    customer.membership == "gold" AND
    cart.total > 100
then
    apply_discount(15)
    add_free_shipping()
end

rule "new_user_welcome"
when
    customer.signup_date < 7_days_ago AND
    customer.purchase_count == 0
then
    apply_discount(20)
    send_welcome_email()
end
            

Performance Metrics

Rule evaluation: <1ms average
Throughput: 100K+ evaluations/second
Rule hot-reload: <100ms
Memory usage: <100MB for 1000 rules

View Documentation