4 dépôts
Awesome GitHub RepositoriesExperimentation Workflows
Methodologies and processes for designing, executing, and evaluating machine learning experiments.
Distinguishing note: Focuses on the procedural workflow of ML research rather than specific model architectures or training code.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Experimentation Workflows. Refine with filters or upvote what's useful.
Awesome Experimentation Workflows GitHub Repositories
Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
- pyg-team/pytorch_geometric
pyg-team/pytorch_geometric
23,838Voir sur GitHubThis project is a deep learning library designed for training neural networks on irregular data structures, including graphs, 3D meshes, and point clouds. It functions as an extension to the PyTorch framework, providing specialized layers and kernels that enable the processing of complex, non-Euclidean information. The library distinguishes itself through a geometric deep learning toolkit that manages the unique requirements of graph-based data. It utilizes sparse matrix-based message passing to aggregate information across nodes and employs dynamic computational graph construction to accommo
Tracks, benchmarks, and manages deep learning experiments using structured interfaces to maintain clear records of performance results.
Pythondeep-learninggeometric-deep-learninggraph-convolutional-networks - vibrantlabsai/ragas
vibrantlabsai/ragas
12,659Voir sur GitHubRagas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin
Orchestrates systematic testing of pipelines by wrapping system functions to capture inputs and performance metrics.
Pythonevaluationllmllmops - growthbook/growthbook
growthbook/growthbook
7,351Voir sur GitHubGrowthBook is a feature flagging and experimentation platform that utilizes a warehouse-native approach to data analysis. It serves as a system for managing feature rollouts and conducting A/B tests by executing SQL queries directly against existing data warehouses to calculate experiment results. The platform is distinguished by its integration of a Model Context Protocol server, which allows AI coding assistants and IDEs to manage flags and query analytics using natural language. It also provides specialized capabilities for AI model optimization, enabling the testing of prompts and models
Scales testing processes through shared metrics, templates, approval workflows, and statistical guardrails.
TypeScriptab-testingabtestabtesting - crisxuan/bestjavaer
crisxuan/bestJavaer
6,604Voir sur GitHubThis project is an AI development knowledge base and engineering resource hub. it serves as a technical documentation archive and a lab for experimenting with large language model agents. The repository functions as a curated directory of evaluated AI tools and resources. It documents practical coding workflows and records experimental observations to establish best practices for building and deploying AI-powered applications. The project covers broad capability areas including AI tool curation, technical knowledge archiving of core computing concepts, and the maintenance of developer guidel
Tracks the chronological progression of agent development and model behavior to refine automation workflows.
Python
