seldon-core
Appwrite
seldon-core | Appwrite | |
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14 | 581 | |
4,220 | 41,134 | |
1.2% | 1.4% | |
7.6 | 10.0 | |
5 days ago | 5 days ago | |
HTML | TypeScript | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" 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 โ 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.
Appwrite
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How I use Appwrite Databases with Pinia to build my own habit tracker
If you haven't tried Appwrite, make sure you give it a spin. It's a open source backend that packs authentication, databases, storage, serverless functions, and all kinds of utilities in a neat API. Appwrite can be self-hosted, or you can use Appwrite Cloud starting with a generous free plan.
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Exploring Appwrite: A Comprehensive Guide
What is Appwrite? Appwrite is an open-source backend server that abstracts the complexity of backend development, allowing developers to focus on building their applications. It provides a wide range of services including databases, storage, functions, and authentication, all designed to work seamlessly together. This integration simplifies the development process, reducing the need for extensive configuration and integration work.
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11 Planetscale alternatives with free tiers
Appwrite is an open source BaaS platform that provides services like serverless functions, serverless databases, user authentication, and messaging. Since its release, it has quickly become a popular choice for building websites and applications.
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Biometric authentication with Passkeys
Appwrite for user management, databases, and serverless functions
- Appwrite
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100+ FREE Resources Every Web Developer Must Try
Appwrite: Open-source backend server for web and mobile developers.
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The 2024 Web Hosting Report
Today, this ecosystem is going strong with new providers like Hasura, AppWrite and Supabase powering millions of projects. There are a few reasons people choose this style of hosting, especially if they are more comfortable with frontend development. BaaS lets them set up a database in a secure way, expose some business logic on top of the data, and connect via a dev-friendly SDK from their app or website code to save data easily. These modern tools build a blend of managed database with curated plugins such as authentication, great admin dashboards, and function as a service type capability - all in one package, and often offered as a integrated hosted service.
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Why would you use Backend as a Service (BaaS)?
View on GitHub
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2024 Web Development Wish List
Joins - see Future of Queries - MariaDB supports json joins, so definitely possible!
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Show HN: Mutable.ai โ Turn your codebase into a Wiki
Wow, looks nice! I almost felt like I could understand Bitcoins code xD
Could you do Appwrite? https://github.com/appwrite/appwrite
I'm not affiliated to them, just wanted to get started hacking it.
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!
supabase - The open source Firebase alternative.
MLServer - An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
Strapi - ๐ Strapi is the leading open-source headless CMS. Itโs 100% JavaScript/TypeScript, fully customizable and developer-first.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
pocketbase - Open Source realtime backend in 1 file
great_expectations - Always know what to expect from your data.
nhost - The Open Source Firebase Alternative with GraphQL.
alibi-detect - Algorithms for outlier, adversarial and drift detection
Directus - The Modern Data Stack ๐ฐ โ Directus is an instant REST+GraphQL API and intuitive no-code data collaboration app for any SQL database.
transformers - ๐ค Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
parse-server - Parse Server for Node.js / Express