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- 15+ Open-Source Tools to Monitor Your Large Language Models (LLMs)
15+ Open-Source Tools to Monitor Your Large Language Models (LLMs)
Essential Tools for LLM Interpretation, Monitoring, and Bias Mitigation
We have curated a list of open-source tools that solve some of the most pressing problems with LLM monitoring and observability:
AllenNLP Interpret: A library for interpreting and visualizing predictions from Large Language Models (LLMs), suitable for model explanation and debugging across various models.
LangKit: An open-source toolkit for monitoring LLMs, offering tools for text quality assessment, hallucination detection, and analysis of sentiment and toxicity.
BERTViz: Designed for visualizing attention mechanisms in BERT-based LLMs and other NLP models such as BERT, GPT-2, and BART.
SHAP (SHapley Additive exPlanations): Applies a game-theoretic approach to explain the outputs of machine learning models, including those from the transformers library by HuggingFace.
AI Fairness 360: A toolkit for identifying, documenting, and mitigating bias and discrimination throughout the machine learning model lifecycle.
Prometheus: An open-source system for collecting and analyzing real-time metrics from LLMs.
Grafana: Works with monitoring systems like Prometheus and Elasticsearch to analyze and visualize metrics and logs from LLMs.
Evidently: A Python library aimed at evaluating, testing, and monitoring NLP and LLM-powered systems, supporting various data types including tabular data, text, and embeddings.
Deepchecks: Offers a solution for validating AI & ML models and data, enabling thorough testing from research to production.
Giskard: Automatically identifies and manages risks in ML models and LLMs, providing coverage for performance and security metrics.
whylogs: Logs any type of data, allowing users to create summaries of their datasets.
lunary: Focuses on observability and prompt management for LLMs, aiding in collaborative debugging and development of LLM applications.
Arize Phoenix: Offers insights for MLOps and LLMOps with a focus on monitoring models and LLM applications through a notebook-first approach.
Pezzo: A cloud-native platform for LLMOps, designed for monitoring AI operations, troubleshooting, reducing costs and latency, managing prompts, and delivering AI updates.
Langfuse: Aids teams in debugging, analyzing, and iterating on LLM applications through collaborative efforts.
Fiddler Auditor: Allows testing of LLMs and NLP models to identify and address potential weaknesses and prevent adversarial outcomes before production deployment.
OpenLLMetry: Built on OpenTelemetry, this extension provides comprehensive observability for LLM applications, compatible with existing observability solutions like Datadog and Honeycomb.
We post helpful lists and bite-sized explanations daily on our X (Twitter). Let’s connect!
15+ open-source tools to monitor your large language models (LLMs)
- AllenNLP Interpret
- LangKit
- BERTViz
- SHAP (SHapley Additive exPlanations)
- AI Fairness 360
- Prometheus
- Grafana
- Evidently
- Deepchecks
- Giskard
- whylogs
- lunary
- Arize Phoenix
- Pezzo
- Langfuse
-… twitter.com/i/web/status/1…— TuringPost (@TheTuringPost)
Mar 25, 2024
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