whylogs
lineapy
whylogs | lineapy | |
---|---|---|
6 | 7 | |
2,548 | 656 | |
0.9% | 0.5% | |
9.0 | 2.0 | |
3 days ago | 9 months ago | |
Jupyter Notebook | Jupyter Notebook | |
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
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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.
lineapy
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Rant: Jupyter notebooks are trash.
There are a few projects that can help close this gap between notebook prototype -> production. One of them is ipyflow (https://github.com/ipyflow/ipyflow), another is lineapy (https://github.com/linealabs/lineapy).
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The hand-picked selection of the best Python libraries and tools of 2022
LineaPy — notebooks in production
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Model artifacts mess and how to deal with it?
If you are mainly using python, there is a library called lineapy that is pretty much trying to solve all the challenges you just listed.
- lineapy: Data engineering, simplified. LineaPy creates a frictionless path for taking your data science artifact from development to production.
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Overwhelmed about consolidating code
Hi, I'm a contributor of LineaPy. We're building a tool that solves this problem. Our goal is to reduce the friction between developing Jupyter notebooks(or python scripts) and production codes.
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When to use Jupyter Notebooks vs. “Organized” Python Code?
I think you might want to give LineaPy a try! It is a tool trying to bridge the gap between Jupyter notebooks and production pipelines. One of the feature it provides is extracting codes only related to objects(you've selected) from your notebook into a python script and I think it is helpful for anyone who is using both Jupyter notebooks and python scripts.
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Introducing LineaPy!
GitHub
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
ruff - An extremely fast Python linter and code formatter, written in Rust.
graphsignal-python - Graphsignal Tracer for Python
lingua-py - The most accurate natural language detection library for Python, suitable for short text and mixed-language text
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
python-benedict - :blue_book: dict subclass with keylist/keypath support, built-in I/O operations (base64, csv, html, ini, json, pickle, plist, query-string, toml, xls, xml, yaml), s3 support and many utilities.
datatap-python - Focus on Algorithm Design, Not on Data Wrangling
ipyflow - A reactive Python kernel for Jupyter notebooks.
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.