Production Ml
Showing 30 of 30 projects
An end-to-end open source platform for machine learning with a comprehensive ecosystem of tools and libraries.
Industrial-strength Natural Language Processing library for Python, featuring pretrained pipelines for 70+ languages and production-ready training.
A deep learning framework to pretrain and finetune any AI model at any scale with zero code changes.
A deep learning framework to pretrain and finetune any AI model on any hardware with zero code changes.
A PyTorch wrapper that automates engineering boilerplate for scalable AI model training and deployment.
A curated collection of papers and articles from companies sharing real-world data science and machine learning applications in production.
A repository of examples, utilities, and best practices for building and deploying production-ready recommendation systems.
A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
A low-code declarative framework for building custom LLMs, neural networks, and other AI models with YAML configurations.
A practical booklet covering the four main steps of designing machine learning systems with 27 interview questions.
A Python library for building production-ready model inference APIs, job queues, and multi-model serving systems for AI applications.
A lightweight, modular, and scalable deep learning framework built on the original Caffe.
A platform for deploying, managing, and scaling machine learning models in production on AWS infrastructure.
An MLOps framework to package, deploy, monitor, and manage thousands of production machine learning models on Kubernetes.
An open-source library for training and deploying deep learning recommendation models with sparse data at scale using multi-GPU support.
An open-source LLMOps platform for prompt management, evaluation, and observability to build reliable LLM applications faster.
An end-to-end platform for applied reinforcement learning and contextual bandits, originally developed at Facebook for production recommendation systems.
An end-to-end platform for applied reinforcement learning and contextual bandits, built with PyTorch for production decision-making systems.
A collection of models, callbacks, and datasets to extend PyTorch Lightning for applied AI/ML research and production.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
MLeap is a portable execution engine for deploying machine learning pipelines from Spark and Scikit-learn without their runtime dependencies.
A curated list of articles covering software engineering best practices for building production machine learning applications.
A Python framework for scalable time series forecasting using machine learning models, designed for production environments.
A uniform interface to run deep learning models from multiple frameworks like TensorFlow, PyTorch, and Keras in C++ and Python.
An open-source machine learning framework built in Rust for high-performance and extensible ML tasks.
A Python library for monitoring model and data drift over time, generating insightful HTML reports for AI governance.
A Clojure library for machine learning and statistical inference designed for production deployment and composable algorithms.
A Python feature engineering engine that internally manages past dependent values for continuous calculation of time-based features.
A Go library for scoring machine learning models using PMML, supporting neural networks, decision trees, random forests, and gradient boosted models.
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