whylogs
elementary
whylogs | elementary | |
---|---|---|
6 | 30 | |
2,548 | 1,739 | |
0.9% | 1.7% | |
9.0 | 9.8 | |
3 days ago | 6 days ago | |
Jupyter Notebook | HTML | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
whylogs
-
The hand-picked selection of the best Python libraries and tools of 2022
whylogs — model monitoring
-
Data Validation tools
Have a look at whylogs. Nice profiling functionality incl. definition of constraints on profiles: https://github.com/whylabs/whylogs
- [D] Open Source ML Organisations to contribute to?
- whylogs: The open standard for data logging
-
I am Alessya Visnjic, co-founder and CEO of WhyLabs. I am here to talk about MLOps, AI Observability and our recent product announcements. Ask me anything!
WhyLabs has an open-source first approach. We maintain an open standard for data and ML logging https://github.com/whylabs/whylogs, which allows anybody to begin logging statistical properties of data in their data pipeline, ML inference, feature stores, etc. These statistical profiles capture all the key signals to enable observability in a given component. This unique approach means that we can run a fully SaaS service, which allows for huge scalability (in both the size of models and their number), and ensures that our customers are able to maintain their data autonomy. We maintain a huge array of integrations for whylogs, including Python, Spark, Kafka, Ray, Flask, MLflow, Kubeflow, etc… Once the profiles are captured systematically, they are centralized in the WhyLabs platform, where we organize them, run forecasting and anomaly detection on each metric, and surface alerts to users. The platform itself has a zero-config design philosophy, meaning all monitoring configurations can be set up using smart baselines and require no manual configuration. The TL;DR here is the focus on open source integrations, working with data at massive/streaming scale, and removing manual effort from maintaining configuration.
-
Machine learning’s crumbling foundations – by Cory Doctorow
This is why we've been trying to encourage people to think about lightweight data logging as a mitigation for data quality problems. Similar to how we monitor applications with Prometheus, we should approach ML monitoring with the same rigor.
Disclaimer: I'm one of the authors. We spend a lot of effort to build the standard for data logging here: https://github.com/whylabs/whylogs. It's meant to be a lightweight and open standard for collecting statistical signatures of your data without having to run SQL/expensive analysis.
elementary
-
Open source data observability tools with UI?
Check out https://github.com/elementary-data/elementary
-
Data Validation tools
In this case, do https://github.com/elementary-data/elementary or https://greatexpectations.io help?
-
SQL “Visualization” Website/Resource?
That makes explain little easier to read. No graph though. Also https://github.com/elementary-data/elementary should know howto draw pretty graphs for data lineage ( ie. what columns comes where and is used how)
- Open source dbt tests monitoring
-
Suggestions for open source anomaly-detection, linting and metadata solutions?
there is elementary lineage / elementary-data which seems to be good try to solve those problem, i havent tested it well https://github.com/elementary-data/elementary
-
Snowflake SQL AST parser?
Some things you might be interested in are re_data and Elementary Data.
- Launch HN: Elementary (YC W22) – Open-source data observability
-
Data lineage info to a table in the DWH
Hi all, As part of building Elementary (open source data reliability), we implemented support of a new Snowflake feature (write operations in the access_history view). The change they made is most useful for understanding data lineage, which we solve (among other use cases :)).
-
Launch HN: Metaplane (YC W20) – Datadog for Data
I recently stumbled on an open-source tool with a similar premise: https://github.com/elementary-data/elementary-lineage
you can check it out
-
Lightweight data profiling tools / relationship discovery
Hi! we are working on an open source data lineage solution that might be helpful for your use case to learn the relationship between tables, we don't support column level just yet but we are working on it. Please let me know if we can help somehow and feel free to check it out here - https://github.com/elementary-data/elementary-lineage
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
re_data - re_data - fix data issues before your users & CEO would discover them 😊
graphsignal-python - Graphsignal Tracer for Python
sqllineage - SQL Lineage Analysis Tool powered by Python
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
dbt-data-reliability - dbt package that is part of Elementary, the dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
tiddlywiki-docker - Tools for running TiddlyWiki via a Docker container
datatap-python - Focus on Algorithm Design, Not on Data Wrangling
lightdash - Self-serve BI to 10x your data team ⚡️
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.