seldon-core
Tailwind CSS
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4,220 | 78,568 | |
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7.6 | 9.4 | |
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GNU General Public License v3.0 or later | MIT License |
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seldon-core
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seldon-core VS MLDrop - a user suggested alternative
2 projects | 20 Feb 2023
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[D] Feedback on a worked Continuous Deployment Example (CI/CD/CT)
ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows. Seldon Core is a production grade open source model serving platform. It packs a wide range of features built around deploying models to REST/GRPC microservices that include monitoring and logging, model explainers, outlier detectors and various continuous deployment strategies such as A/B testing, canary deployments and more.
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[D] BentoML's Compatibility with Seldon;
I am using BentoML to build the docker container for a BERT model, and then deploy that using Seldon on GKE. The model's REST API endpoint works fine. at terms of compatibility with Seldon, the metrics are being scraped by Prometheus and visualized on Grafana. The only Seldon component that doesn't appear to be working is the request logging, which I have working for other applications that were deployed on Seldon. I am using the elastic stack from here. From my understanding, request logging should still be compatible and the ⠀only lost functionality should be Seldon's model metadata. Any insight on how to get the centralized request logging working? No errors were shown; it's just that the logs aren't being captured and sent to ElasticSearch. Anyone have any success using BentoML with Seldon and not losing any of Seldon's features?
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Building a Responsible AI Solution - Principles into Practice
While tools in the model experimentation space normally include diagnostic charts on a model's performance, there are also specialised solutions that help ensure that the deployed model continues to perform as they are expected to. This includes the likes of seldon-core, why-labs and fiddler.ai.
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Ask HN: Who is hiring? (January 2022)
Seldon | Multiple positions | London/Cambridge UK | Onsite/Remote | Full time | seldon.io
At Seldon we are building industry leading solutions for deploying, monitoring, and explaining machine learning models. We are an open-core company with several successful open source projects like:
* https://github.com/SeldonIO/seldon-core
* https://github.com/SeldonIO/mlserver
* https://github.com/SeldonIO/alibi
* https://github.com/SeldonIO/alibi-detect
* https://github.com/SeldonIO/tempo
We are hiring for a range of positions, including software engineers(go, k8s), ml engineers (python, go), frontend engineers (js), UX designer, and product managers. All open positions can be found at https://www.seldon.io/careers/
- Ask HN: Who is hiring? (December 2021)
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Has anyone implemented Seldon?
Also note our github repo has a link to our slack where you can ask active users: https://github.com/SeldonIO/seldon-core
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[Discussion] Look for service to upload a model and receive a REST API endpoint, for serving predictions
If you want to serve your model at scale, with a bunch of production features you should have a look at the open-source framework Seldon Core. It does what you're asking for plus a bunch of other cool stuff like routing, logging and monitoring.
- Seldon Core : Open-source platform for rapidly deploying machine learning models on Kubernetes
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Looking for open-source model serving framework with dashboard for test data quality
Seldon ticks most of those boxes if you already have some experience with kubernetes. You can set up a/b tests, do payload logging to elastic and then do monitoring on top of that, and it has drift detection and model explainer modules too. Idk about great expectations integration, but you could probably do something with a custom transformer module as part of the inference graph.
Tailwind CSS
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How to Build Your Own ChatGPT Clone Using React & AWS Bedrock
Finally, for our front end, we’re going to be pairing Next.js with the great combination of TailwindCSS and shadcn/ui so we can focus on building the functionality of the app and let them handle making it look awesome!
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Building an Email Assistant Application with Burr
You can use any frontend framework you want — react-based tooling, however, has a natural advantage as it models everything as a function of state, which can map 1:1 with the concept in Burr. In the demo app we use react, react-query, and tailwind, but we’ll be skipping over this largely (it is not central to the purpose of the post).
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Shared Data-Layer Setup For Micro Frontend Application with Nx Workspace
Tailwind CSS: A utility-first CSS framework for rapidly building custom designs.
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Preline UI + Gowebly CLI = ❤️
First, you need to make sure that you have a working Tailwind CSS project…
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Customer service pages for e-commerce built with Tailwind CSS
Tailwind CSS
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The best testing strategies for frontends
With better CSS approaches like TailwindCSS and Vanilla Extract (which we're heavily using) it's much easier to maintain the UI and make sure it doesn't change unexpectedly. No more conflicting CSS classes, much less CSS specificity issues and much less CSS code in general.
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ChatCrafters - Chat with AI powered personas
This app was built with Svelte Kit, Tailwind CSS, and many other technologies. For a full rundown, please visit the GitHub repository
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Mojo CSS vs. Tailwind: Choosing the best CSS framework
Unlike Tailwind, which has over 77,000 stars on GitHub, Mojo CSS has about 200 stars on GitHub. But the Mojo CSS documentation is fairly good and you can find most of the information you’ll need there.
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Collab Lab #66 Recap
JavaScript React Flowbite Tailwind Firebase - Auth, Database, and Hosting Vite
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Show HN: Brutalisthackernews.com – A HN reader inspired by brutalist web design
- Performance is a feature.
Another common interpretation of brutalism is aesthetic, reacting to overly complicated user interfaces by creating simpler, more direct ones. Tailwind CSS (https://tailwindcss.com), one of today's most popular CSS libraries, promotes this approach in its component examples. There's also a neat library I've seen recently called "Neobrutalism Components" for React that I like (https://neobrutalism-components.vercel.app), providing components with a similar look and feel to Gumroad. This might more accurately be called 'Neo-Brutalism,' as noted in the comments.
A more engineering-centric interpretation of Brutalism focuses on form, structure, and efficiency, drawing significantly from brutalist architecture principles. Apart from the user interface itself, most mobile, desktop, and web applications are extremely bloated and often perform worse than sites from 10 years ago did. While one HTML file might be "less brutalist" than the original HN site, it is substantially more brutalist than any HN mobile app in existence, and offers nearly identical functionality.
A broader interpretation of brutalism, which could be termed 'Meta-Brutalism,' is embodied in the overall experience on this site through UX flows. Yes, in the strictest sense, the original HN site is more Brutalist in many ways, but it only shows 30 articles at a time and does not function as a PWA. For this site, the experience of reading 10 stories is arguably less brutalist, but for quickly browsing through several pages and skimming articles (which is how I read HN) it is a lot faster, and in my opinion, more Brutalist.
My primary inspiration was addressing software and tool bloat in UIs rather than strictly adhering to every principle set forth by David Bryant Copeland. I don't find it convincing that this site "isn't brutalist" compared to really any other experience apart from the Main HN site, and I would argue the overall experience is more brutalist in its performance and scrolling behavior.
As a side note: I generally don't like Brutalist architecture that much although I believe it is unfairly maligned. I visited the Salk Institute once and enjoyed it though (https://www.archdaily.com/61288/ad-classics-salk-institute-l...).
What are some alternatives?
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!
flowbite - Open-source UI component library and front-end development framework based on Tailwind CSS
MLServer - An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
antd - An enterprise-class UI design language and React UI library
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
unocss - The instant on-demand atomic CSS engine.
great_expectations - Always know what to expect from your data.
windicss - Next generation utility-first CSS framework.
alibi-detect - Algorithms for outlier, adversarial and drift detection
emotion - 👩🎤 CSS-in-JS library designed for high performance style composition
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Material UI - Ready-to-use foundational React components, free forever. It includes Material UI, which implements Google's Material Design.