A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

A comprehensive guide designed to empower readers with advanced strategies and practical insights for developing, optimizing, and deploying scalable AI models in real-world applications.

Репозиторий направления Production ML, весна 2021

Lead Scoring: Optimizing SaaS Marketing-Sales Funnel by Extracting the Best Leads with Applied Machine Learning

Real-time fraud detection system using ensemble ML models, featuring streaming data processing, explainable AI with SHAP, and production-ready deployment with FastAPI and Docker.

This project is made to help you scale from a basic Machine Learning project for research purposes to a production grade Machine Learning web service

The objective of this coding exercice is to train a simple neural network on the mnist dataset in order to classify the handwritten digits into numbers ranging from zero to 9.

Production-ready ML regression system for restaurant rating prediction (0-5 stars) with exceptional performance: RMSE 0.123, R² 0.954, 96% accuracy within ±0.25 stars. Features FastAPI backend, interactive frontend, and comprehensive MLOps pipeline.

Using machine learning and applied analytics to identify high-residual opioid prescribers

Enterprise Text Classification Model Selection Framework Automated decision-support system for selecting optimal transformer models in production text pipelines. Evaluates BERT, DistilBERT, and ELECTRA across accuracy, speed, and cost metrics for finance, healthcare, legal, and customer service applications.

🛡️ Production ML Fraud Detection System | HW_10 OTUS MLOps: Kubernetes автоскейлинг, Prometheus мониторинг, Airflow ML пайплайны

Hands-on project to learn MLOps fundamentals with GCP-native services (Vertex AI, Cloud Run, Cloud Functions, Cloud Build, GCS) using Fashion-MNIST dataset

95% accurate weather image classifier with TensorFlow and Grad-CAM. Demonstrates production ready ML skills: transfer learning, data augmentation, interpretability, and error analysis.

🤖 Detect fraudulent transactions in real time with our AI system, reducing losses and providing clear explanations for compliance.

Production-ready customer segmentation system with interactive Streamlit dashboard, FastAPI REST API, and SHAP explainability

End-to-end MLOps pipeline for customer satisfaction prediction. Features automated training, continuous deployment with MLflow, experiment tracking with ZenML and interactive predictions via Streamlit. Production-ready ML workflow with model versioning and reproducibility.

Built on peer-reviewed research accepted at IEEE DSA 2025: "Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring: Empirical Validation and Temporal Stability Analysis"

🛡️ Build a robust fraud detection system with ensemble machine learning models for real-time insights and explainable AI.

🛡️ Build a production-ready ML system for fraud detection with auto-scaling, monitoring, and orchestration using Kubernetes on Yandex Cloud.