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The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, 17+ clouds, or on-prem).
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
Ultrafast serverless GPU inference, sandboxes, and background jobs
🎨 UI for monitoring your Metaflow executions!
🚀 Metadata tracking and UI service for Metaflow!
Finetune LLMs on K8s by using Runbooks
MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain.
MONAI Deploy aims to become the de-facto standard for developing, packaging, testing, deploying and running medical AI applications in clinical production.
The official python package for NimbleBox. Exposes all APIs as CLIs and contains modules to make ML 🌸
Run GPU or batch jobs directly from your dev environment
cluster/scheduler health monitoring for GPU jobs on k8s
"1 config, 1 command from Jupyter Notebook to serve Millions of users", Full-stack On-Premises MLOps system for Computer Vision from Data versioning to Model monitoring and drift detection.
A cloud-agnostic ML Platform that will enable Data Scientists to run multiple experiments, perform hyper parameter optimization, evaluate results and serve models (batch/realtime) while still maintaining a uniform development UX across cloud environments
A "production-ready" simple project template to quickly start an Artificial Intelligence (AI), Machine Learning (ML) and/or Data Science (DS) project with basic files, branches and directory structure.
Welcome to the Machine Learning Engineering Repository, a comprehensive collection of resources, code, and insights to guide you through the exciting world of machine learning. This repository is designed to provide valuable information, best practices, and hands-on examples for individuals keen on mastering the art and science of machine learning
Quality Control for AI Artifact Management
Resources on real-time machine learning
Example notebooks using the TurboML platform