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Inference for text-embedding models.
Simplifies and accelerates MLOps by bridging the gap between ML models and edge hardware.
MyVibe supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Milvus is open source vector database for production AI, written in Go and C++, scalable and blazing fast for billions of embedding vectors.
Ship AI agents that reason, remember, and act in TypeScript. Mastra provides memory, tools, MCP, and observability to go from prototype to production.
A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.
Open-source all-in-one platform for engineering AI products. Traces, Evals, Datasets, Labels.
KubeStellar Console supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Kubeflow makes deployment of ML Workflows on Kubernetes straightforward and automated.
Standardized Serverless ML Inference Platform on Kubernetes.
Open source MLOps project that eases model handoffs between data scientist and DevOps.
Kubernetes custom resource definition for serving ML models on arbitrary frameworks.
Kubernetes-based system for hyperparameter tuning and neural architecture search.
10x faster, cheaper, and better vector database.
Open-source platform for high-performance ML model serving.
Run sandboxes, task queues, and custom model inference with ultrafast boot times, instant autoscaling, and a developer experience that just works.
An educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning.
My daily sufferings as a PhD student at Berkeley.
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.
An open source implementation of methods for multi-label classification and evaluation (extension to Weka).
A Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback.
Simple API for Neural Network. Better for image processing with CPU/GPU + Transfer Learning.
A beginner-friendly guide on using Keras to implement a simple Neural Network in Python.