Machine Learning Model Deployment Platform

Software Personal Project

Overview

A scalable platform for deploying and monitoring machine learning models in production with automated CI/CD pipelines.

Technologies Used

  • Python
  • Docker
  • Kubernetes
  • FastAPI
  • PostgreSQL
  • Redis
  • GitHub Actions

Links

Project Overview

This project provides a complete solution for deploying machine learning models to production. It includes REST APIs for model serving, monitoring dashboards, and automated deployment pipelines.

Key Features

  • Model Serving: High-performance REST API for model inference
  • Version Control: Track and manage multiple model versions
  • A/B Testing: Built-in support for comparing model performance
  • Monitoring: Real-time metrics and logging for model predictions
  • Auto-scaling: Kubernetes-based automatic scaling based on load
  • CI/CD Pipeline: Automated testing and deployment using GitHub Actions

Technical Architecture

Backend Services

  • API Gateway: FastAPI-based service for handling inference requests
  • Model Registry: Centralized storage and versioning of models
  • Monitoring Service: Collects and aggregates prediction metrics
  • Database: PostgreSQL for metadata, Redis for caching

Infrastructure

The platform runs on Kubernetes, providing:

  • Horizontal pod autoscaling
  • Rolling updates with zero downtime
  • Health checks and automatic recovery
  • Resource isolation and management

Implementation Details

Model Serving

Models are containerized using Docker and can be deployed with a simple configuration file:

model:
  name: "image-classifier"
  version: "v1.0.0"
  framework: "pytorch"
  resources:
    cpu: "1000m"
    memory: "2Gi"

API Design

The platform exposes a simple REST API:

  • POST /predict: Submit data for prediction
  • GET /models: List available models
  • GET /metrics: Retrieve model performance metrics

Performance

The platform handles 1000+ requests per second with average latency under 100ms. Auto-scaling maintains performance during traffic spikes.

Monitoring and Observability

Integrated monitoring tracks:

  • Request latency and throughput
  • Model prediction accuracy
  • Resource utilization
  • Error rates and types

Future Enhancements

  • Support for streaming predictions
  • Multi-cloud deployment
  • Advanced feature store integration
  • Automated model retraining pipelines