AI Observability & Evaluation
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Updated
Jun 6, 2024 - Jupyter Notebook
AI Observability & Evaluation
🌀 𝗧𝗵𝗲 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝟳-𝗦𝘁𝗲𝗽𝘀 𝗠𝗟𝗢𝗽𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 | 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟𝗘 & 𝗠𝗟𝗢𝗽𝘀 for free by designing, building and deploying an end-to-end ML batch system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 2.5 𝘩𝘰𝘶𝘳𝘴 𝘰𝘧 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 & 𝘷𝘪𝘥𝘦𝘰 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴
Azure Databricks MLOps sample for Python based source code using MLflow without using MLflow Project.
Product analytics for AI Assistants
Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.
Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.
A python library to send data to Arize AI!
[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
Python SDK for vishwa.ai
Example projects for Arthur Model Monitoring Platform
🔥🔥🔥🔥🧊🔥🔥 A Data Platform for Monitoring and Detecting Anomalies in Real-Time.
🕵️🤖 Monitoring a PyTorch Lightning CNN with Weights & Biases
An agent that exports telemetry for served ML models in TFServing and KFServing.
🌐 Language identification for Scandinavian languages
ML Monitoring with EvidentlyAI
Integrating Aporia ML model monitoring into a Bodywork serving pipeline.
Java client to interact with Arize API
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