Experiment Tracking
Showing 29 of 29 projects
An open-source AI engineering platform for debugging, evaluating, monitoring, and optimizing production LLM applications and machine learning models.
A Python framework for creating reproducible, maintainable, and modular data engineering and data science pipelines.
A framework for elegantly configuring complex applications, particularly in machine learning and research.
A library that enables PyTorch, Chainer, MXNet, and NumPy users to write TensorBoard events with simple function calls.
A suite of web applications for inspecting and understanding TensorFlow runs and graphs.
An open-source MLOps/LLMOps suite for experiment management, data management, pipelines, orchestration, scheduling, and model serving.
An open-source, self-hosted ML experiment tracker with a performant UI and SDK for comparing and querying training runs.
An open-source MLOps platform for building, orchestrating, and deploying production AI pipelines and agents.
An open-source platform for building, training, and monitoring large-scale deep learning applications with full lifecycle MLOps.
A TensorFlow project template with a well-designed folder structure and OOP design to accelerate deep learning development.
An open-source machine learning platform for distributed training, hyperparameter tuning, experiment tracking, and resource management.
A toolkit and library for developing, evaluating, and reproducing reinforcement learning algorithms.
A foundational PyTorch library for training deep learning models, serving as the core engine for the OpenMMLab ecosystem.
A hands-on tutorial for training and deploying a machine learning model as a serverless REST API to predict cryptocurrency prices.
IPython-based environment for reproducible machine learning research with unified wrappers for multiple ML libraries.
Open-source teaching materials for a practical Machine Learning in Finance course, focusing on industry tools and real-world use cases.
An engine for ML/data tracking, visualization, explainability, drift detection, and dashboards, integrated with Polyaxon.
An open-source machine learning solution for the Home Credit Default Risk Kaggle competition, providing reproducible code and experiments.
A Python library for logging ML metrics, parameters, and models in simple file formats, compatible with DVC and Git.
A collection of examples demonstrating how to use Comet.ml for machine learning experiment tracking across various Python frameworks.
An open-source benchmark solution for the Kaggle TGS Salt Identification Challenge using semantic segmentation.
An open-source solution for the Airbus Ship Detection Challenge, providing a benchmark and base for ship detection in satellite imagery.
A GitHub Action that retrieves model runs and metrics from Weights & Biases for integration into CI/CD workflows.
An open-source solution for the Google AI Open Images Object Detection Challenge, providing a RetinaNet-based benchmark with experiment tracking.
An open-source machine learning solution for the Santander Value Prediction Challenge on Kaggle.
A lightweight logging library for machine learning research experiments.
A VS Code extension to view and compare Weights & Biases training runs directly in the editor with interactive charts and AI context export.
A Python library for comprehensive machine learning model evaluation with plots, reports, experiment tracking, and notebook analysis.
JAX/Haiku implementation of a two-player game approach to learning optimal multi-bidder, multi-item auctions.
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