About
Open Source Tracing and Evaluation for LLM App Development
Phoenix provides MLOps and LLMOps insights at lightning speed with zero-config observability. Phoenix provides a notebook-first experience for monitoring your models and LLM Applications by providing:
LLM Traces - Trace through the execution of your LLM Application to understand the internals of your LLM Application and to troubleshoot problems related to things like retrieval and tool execution.
LLM Evals - Leverage the power of large language models to evaluate your generative model or application's relevance, toxicity, and more.
Embedding Analysis - Explore embedding point-clouds and identify clusters of high drift and performance degradation.
RAG Analysis - Visualize your generative application's search and retrieval process to identify problems and improve your RAG pipeline.
Structured Data Analysis - Statistically analyze your structured data by performing A/B analysis, temporal drift analysis, and more.
This product is intended for
- Data Scientist
- Machine Learning Engineer
- Director Data Science
- Machine Learning Researcher
- Senior Machine Learning Engineer
- Head of Machine Learning
- Manager of Machine Learning
- Director of Machine Learning
- Artificial Intelligence Engineer
- Generative AI Engineer
Media
Products media viewer
-
LLM tracing - evaluations and explanations
-
Analyzing Embedding in 3D UMAP
-
Identifying Problematic Cluster in UMAP To Export for Fine Tuning
-
Evaluating LLM Prompts and Responses
-
LLM traces in Arize Phoenix
Similar products
Arize AI products
-
Arize AI | Observability and LLM Evaluation Platform
Arize AI | Observability and LLM Evaluation Platform
Data Science & Machine Learning Platforms