Model Serving
Showing 29 of 29 projects
A platform to run, manage, and serve open-source large language models (LLMs) locally or on your own infrastructure.
A high-throughput, memory-efficient inference and serving engine for large language models (LLMs).
An open platform for training, serving, and evaluating large language model based chatbots.
A minimalist, high-performance machine learning framework for Rust with a focus on serverless inference and GPU support.
A composable, modular, and scalable machine learning toolkit for building AI platforms on Kubernetes.
A low-code declarative framework for building custom LLMs, neural networks, and other AI models with YAML configurations.
An open-source inference serving platform for deploying AI models from multiple frameworks across cloud, data center, and edge devices.
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 platform for deploying, managing, and scaling machine learning models in production on AWS infrastructure.
A fast, flexible, and hardware-aware LLM inference engine with zero-config support for any Hugging Face model.
An open-source MLOps/LLMOps suite for experiment management, data management, pipelines, orchestration, scheduling, and model serving.
A curated collection of resources for building, training, serving, and optimizing production-grade Large Language Model applications.
An MLOps framework to package, deploy, monitor, and manage thousands of production machine learning models on Kubernetes.
An open-source deep learning API and server written in C++ that supports multiple backends like PyTorch, TensorRT, and TensorFlow for training and inference.
A JAX/Flax-based framework for easy and scalable pre-training, fine-tuning, evaluation, and serving of large language models.
A Go library that simplifies TensorFlow's Go bindings with method chaining, automatic scoping, and type conversion.
A command-line tool for creating reproducible, container-based development environments for AI/ML workflows.
A visual workflow-based AI deployment framework for multi-platform and multi-backend inference, supporting large models and edge devices.
An open-source machine learning system for the end-to-end data science lifecycle from data preparation to model serving.
A tool to package, serve, and deploy any ML model on any platform using a GitOps approach.
A Ruby library for building and serving predictive models with support for PMML and integration with Python and R models.
A production-ready FastAPI skeleton app for serving machine learning models with built-in authentication and testing.
A book teaching practical patterns for building scalable and reliable distributed machine learning systems using Kubernetes, TensorFlow, Kubeflow, and Argo Workflows.
A JAX-based framework for streamlined training, fine-tuning, and high-performance serving of large language and multimodal models.
A TensorFlow-based object detection model that localizes and identifies multiple objects in images using SSD MobileNet V1 or Faster R-CNN ResNet101.
A TensorFlow-based model that generates descriptive captions for images using an Inception-v3 encoder and LSTM decoder.
A Docker-based speech recognition model that converts short English WAV audio files into text using Mozilla's DeepSpeech.
A TensorFlow/Keras LSTM model for hourly weather forecasting, offering univariate, multivariate, and multistep prediction modes.
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