phoenix-observability

Streamlines AI research workflows by providing curated Claude skills for data analysis, literature review, experiment design, and research paper generation.

Assists AI researchers in drafting, structuring, and generating machine learning research papers with academic writing best practices and technical precision.

Streamlines distributed machine learning training using Ray, optimizing hyperparameter tuning and parallel model execution across compute clusters.

Streamlines distributed data processing and machine learning workflows using Ray's scalable data loading and transformation capabilities.

Streamlines supervised fine-tuning of language models using Simple, Interpretable, Modular Policy Optimization (SIMPO) techniques

Orchestrates collaborative AI agents using CrewAI to solve complex tasks through dynamic role assignment, task delegation, and intelligent workflow management.

Quantize and compress large language models using GPTQ for efficient inference and reduced memory footprint on various hardware.

Accelerates AI model inference by predicting and parallel processing multiple token candidates to reduce latency and improve generation speed.

Streamlines machine learning experiment tracking, visualization, and hyperparameter optimization using Weights & Biases platform integration

Streamline machine learning experiment tracking, model versioning, and deployment management with comprehensive MLflow integration and best practices.