Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
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Updated
Oct 19, 2024 - Jupyter Notebook
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
Production model monitoring and data profiling for machine learning workflows
⚓ Eurybia monitors model drift over time and securizes model deployment with data validation
Open-source observability for your LLM application, based on OpenTelemetry
Sister project to OpenLLMetry, but in Typescript. Open-source observability for your LLM application, based on OpenTelemetry
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Toolkit for evaluating and monitoring AI models in clinical settings
LLM application tracing based on OpenTelemetry
A python library to send data to Arize AI!
Image Classification on AWS SageMaker using Lambda Functions, Step Functions and Model Monitor
nannyml: post-deployment data science in python
MLOps workshop with Amazon SageMaker
Free MLOps course from DataTalks.Club
Version, share, deploy, and monitor models.
"1 config, 1 command from Jupyter Notebook to serve Millions of users", Full-stack On-Premises MLOps system for Computer Vision from Data versioning to Model monitoring and drift detection.
Evidently AI in tracking, analyzing, and visualizing machine learning model performance and data drift ensure their reliability over time.
Developed an image classification model for Scones Unlimited to identify delivery vehicles (bicycles vs. motorcycles) to enhance routing and loading bay assignments, thereby optimizing operational efficiency.
Projects Implemented for the Udacity Machine Learning DevOps Engineer Nanodegree Program
This repository contains the notes, practice and sample outputs, homeworks, etc, from DataTalks.Club's MLOps Zoomcamp 2024 Cohort
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