beneath
whylogs
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beneath | whylogs | |
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2 | 6 | |
78 | 2,538 | |
- | 1.6% | |
0.0 | 9.1 | |
about 2 years ago | 3 days ago | |
Go | Jupyter Notebook | |
GNU General Public License v3.0 or later | 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.
beneath
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Analyzing the r/wallstreetbets hivemind — August 2021
If you’re interested, here’s the raw Reddit data, my data pipeline, the derived data, and my Jupyter notebook. I’m using Beneath, an open data platform I’m building, to stream and save the data.
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[Self Promotion] Reddit r/wallstreetbets posts and comments in real-time
The scraper (which uses Async PRAW) is open source here: https://github.com/beneath-hq/beneath/tree/master/examples/reddit
whylogs
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The hand-picked selection of the best Python libraries and tools of 2022
whylogs — model monitoring
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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
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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.
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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.
What are some alternatives?
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
optimus - Optimus is an easy-to-use, reliable, and performant workflow orchestrator for data transformation, data modeling, pipelines, and data quality management.
graphsignal-python - Graphsignal Tracer for Python
pachyderm - Data-Centric Pipelines and Data Versioning
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
sayn - Data processing and modelling framework for automating tasks (incl. Python & SQL transformations).
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
datatap-python - Focus on Algorithm Design, Not on Data Wrangling
oomstore - Lightweight and Fast Feature Store Powered by Go (and Rust).
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]