How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
🛠 All-in-one web-based IDE specialized for machine learning and data science.
The main features of ml-tooling/ml-workspace are: AI and Machine Learning, Machine Learning, Machine Learning Platform, Infrastructure and Environments, Infrastructure and Tools, MLOps and Pipelines, Data Science Tooling, Data Science Tools.
Projects with overlapping indexed features include: tensorchord/envd — 🏕️ Reproducible development environment for humans and agents. lutzroeder/netron — Netron is a visualizer for neural network and machine learning models. It provides a graphical interface that renders… iterative/dvc — DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models.… benedekrozemberczki/littleballoffur — Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020). benedekrozemberczki/karateclub — Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020). asavinov/lambdo — Feature engineering and machine learning: together at last!
Netron is a visualizer for neural network and machine learning models. It provides a graphical interface that renders model architectures as interactive node-link diagrams, allowing users to inspect internal layers, tensors, and metadata. By performing static analysis, the tool enables the examination of model definitions without executing the underlying machine learning code. The software distinguishes itself through a schema-driven parsing engine that translates diverse proprietary model formats into a unified internal graph structure. This approach ensures interoperability, allowing users
🏕️ Reproducible development environment for humans and agents
DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models. It functions as a system for managing large data artifacts by storing lightweight metadata in version control while keeping the actual binaries in a separate cache. The project serves as an experiment tracker and remote storage synchronizer, enabling the execution and comparison of machine learning iterations based on hyperparameters and performance metrics. It provides a bridge for pushing and pulling these large data artifacts between local environments and cloud or on-premi
Albumentations is a computer vision image augmentation library designed to increase training data diversity for deep learning models. It provides a toolset for applying geometric and color transformations to images and annotations, including a specialized collection of 3D operations for volumetric data used in medical and scientific imaging. The library functions as an image mask and bounding box transformer, automatically updating masks, bounding boxes, and keypoints when images undergo geometric changes. This ensures that spatial alterations remain synchronized across images and their assoc