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

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

Drop-in PyTorch optimizers with Hamiltonian mechanics for enhanced temporal stability. Features validated StableAdam/StableSGD optimizers and novel research on SGD momentum conservation for neural network training.

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.

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

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

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