python-benedict
whylogs
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python-benedict | whylogs | |
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3 | 6 | |
1,402 | 2,538 | |
- | 1.6% | |
8.9 | 9.1 | |
3 days ago | 4 days ago | |
Python | Jupyter Notebook | |
MIT License | 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.
python-benedict
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What are the best Python libraries to learn for beginners?
python-benedict: Dictionary manipulation library
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The hand-picked selection of the best Python libraries and tools of 2022
python-benedict — a dict on steroids
- Released python-benedict 0.24.0
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?
prodict - Prodict, what Python dict meant to be.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
countries-states-cities-database - 🌍 Discover our global repository of countries, states, and cities! 🏙️ Get comprehensive data in JSON, SQL, PSQL, XML, YAML, and CSV formats. Access ISO2, ISO3 codes, country code, capital, native language, timezones (for countries), and more. #countries #states #cities
graphsignal-python - Graphsignal Tracer for Python
parse_it - A python library for parsing multiple types of config files, envvars & command line arguments that takes the headache out of setting app configurations.
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
basecrack - Decode All Bases - Base Scheme Decoder
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
structured-text-tools - A list of command-line tools for manipulating structured text data
datatap-python - Focus on Algorithm Design, Not on Data Wrangling
bidict - The bidirectional mapping library for Python.
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]