AI Data Science Service
A complete MLOps platform demonstrating industry best practices for data science and machine learning engineering.
Overview
AI Data Science Service is a reference implementation for MLOps and DevOps best practices in machine learning projects. This repository demonstrates the complete lifecycle of ML engineering—from exploratory analysis to production-ready API services. The platform bridges the gap between data science notebooks and production software, showcasing how to structure ML projects for scalability, reproducibility, and maintainability.Key Features
PyTorch Deep Learning
Production-grade neural network models built with PyTorch for credit risk prediction
MLflow Tracking
Complete experiment tracking with model versioning and metrics visualization
FastAPI Inference
High-performance REST API for real-time model inference with automatic documentation
DVC Data Versioning
Version control for datasets with remote storage integration (S3, DagsHub, Azure Blob)
Docker Deployment
Containerized services ensuring consistency from development to production
Modular Architecture
Separation of concerns with distinct modules for training, inference, and preprocessing
YAML Configuration
Flexible model configuration system enabling hyperparameter experimentation without code changes
Type-Safe Schemas
Pydantic validation for robust data contracts and API safety
Architecture Highlights
The platform demonstrates professional ML engineering practices:- Reproducibility: Deterministic environments with UV package management and DVC data versioning
- Observability: Deep MLflow integration for tracking experiments, parameters, and artifacts
- Scalability: Async FastAPI services with Docker Compose orchestration
- Maintainability: Modular codebase with clear separation between training and inference
- Testing: Structured for CI/CD integration with GitHub Actions
Use Cases
Credit Score AI
End-to-end credit risk assessment with PyTorch neural networks
More Use Cases
Explore upcoming ML projects in energy, retail, and medical imaging
What’s Inside
This platform includes:- Training Pipeline: Configurable model training with MLflow experiment tracking
- Inference API: FastAPI service exposing trained models via REST endpoints
- Data Processing: Scikit-learn preprocessing pipelines with joblib serialization
- Model Architecture: Flexible PyTorch neural networks with configurable layers and activations
- Web Client: Demo application showcasing API integration
- Container Images: Production-ready Docker configurations
Next Steps
Quickstart
Get up and running in minutes with our quickstart guide
MLOps Architecture
Understand the MLOps principles and architecture patterns
Training Models
Learn how to train and track ML models
API Reference
Explore the complete API documentation