whylogs
mosec
whylogs | mosec | |
---|---|---|
6 | 11 | |
2,548 | 703 | |
0.9% | 0.9% | |
9.0 | 8.5 | |
2 days ago | 11 days ago | |
Jupyter Notebook | Python | |
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.
mosec
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20x Faster as the Beginning: Introducing pgvecto.rs extension written in Rust
Mosec - A high-performance serving framework for ML models, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine. Simple and faster alternative to NVIDIA Triton.
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[D] Handling Concurrent Request for ML Model API
- Yes C++ would be better, but you can try mosec. It has a Python interface and helps you handle all the difficult things about Python multiprocessing. The web service part is implemented in Rust thus it's fast enough for machine learning services.
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Launching ModelZ Beta!
Contribute to open source projects: Modelz is built on top of envd, mosec, modelz-llm and many other open source projects. If you're interested in contributing to these projects, you can check out their GitHub repositories and start contributing.
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Deploying a model with an API in docker
You could first create the image with the framework you like (e.g. bentoml or https://github.com/mosecorg/mosec for light weight).
- PostgresML is 8-40x faster than Python HTTP microservices
- Python Machine Learning Service Can Run Way More Faster
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[D] Open Source ML Organisations to contribute to?
If you're interested in machine learning model serving, can check mosec: https://github.com/mosecorg/mosec
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Why not multiprocessing
During the development of a machine learning serving project Mosec, I used a lot of multiprocessing to make it more efficient. I want to share some experiences and some researches related to Python multiprocessing.
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[P] Mosec: deploy your machine learning model in an easy and efficient way
That's a good example. I have met the same situation before. I have created a discussion in GitHub to track the DAG progress.
- Mosec: deploy your machine learning model in an easy and efficient way
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
graphsignal-python - Graphsignal Tracer for Python
GPflow - Gaussian processes in TensorFlow
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
mlrun - MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
text-generation-inference - Large Language Model Text Generation Inference
datatap-python - Focus on Algorithm Design, Not on Data Wrangling
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
postgresml - The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.