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.
Examples of Machine Learning code using Comet.ml
The main features of comet-ml/comet-examples are: Experiment Management, General Machine Learning, Model Management, Experiment Tracking, MLOps and Pipelines, Data Science Tooling, Data Science Tools.
Projects with overlapping indexed features include: iterative/dvc — DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models.… iterative/mlem — 🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞. pycaret/pycaret — PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It… astrazeneca/rexmex — A general purpose recommender metrics library for fair evaluation. astrazeneca/chemicalx — A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022). allegroai/clearml — ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an…
🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞
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
PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data. The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp
