seldon-core
Jenkins
seldon-core | Jenkins | |
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14 | 150 | |
4,220 | 22,421 | |
1.2% | 0.7% | |
7.6 | 9.9 | |
5 days ago | 4 days ago | |
HTML | Java | |
GNU General Public License v3.0 or later | MIT License |
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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.
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.
Jenkins
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Most Useful CI/CD Tools for DevOps
Jenkins is a highly extensible Java-based CI/CD automation server. It is open-source and self-hosted and enables you to automate, build, and deploy your software. The tool seamlessly integrates with various version control systems, cloud providers, and third-party applications, making it a versatile choice for modern development environments.
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The Essential Guide to Internal Developer Platforms
For instance, IDPs can automatically trigger a deployment process in Jenkins or CircleCI when a developer pushes code to a Git repository.
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20 Things You Should Consider When You Grow as a Developer
Familiarize yourself with tools like Jenkins, Travis CI, GitLab CI, and others that facilitate these practices.
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Supercharge Your Mobile Dev Skills: 10 Essential Tools for Max Efficiency
Jenkins: An open-source automation server that can be used to set up CI/CD pipelines.
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Building a CI/CD Integration with Amazon EC2, Jenkins and Docker
Jenkins is an open-source automation tool that allows developers to build, test and deploy software.
- Boas práticas para revisão de código
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Leveraging CI/CD for Streamlined Software Development and Deployment
Choosing the right CI/CD platform and mastering YAML configuration are critical steps in optimizing your software development process. First, choose a CI/CD platform from popular options such as Jenkins, Travis CI, CircleCI, GitLab CI/CD, or GitHub Actions that meets the needs of your project and integrates seamlessly with your development stack.
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Jenkins Agents On Kubernetes
Jenkins is a Java based CI/CD system that can be self hosted. In order to initiate builds, Jenkins utilizes a component called an agent to execute build commands. These agents can be a constantly running service or part of an on demand service such as a cloud provider or Docker containers. Kubernetes is one such solution to deploying build agents on demand. This article will look at how to setup Kubernetes as a provider for Jenkins build agents.
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Best CI/CD tools for React Native
In this article, we briefly discussed some popular CI/CD platforms for React Native and why they are crucial in the programming world. We also included some honorable mentions, Jenkins CI and Bitrise, in our comparison table. It is important to remember that every project is different, and therefore it is important to evaluate each tool’s advantages and disadvantages.
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Looking for a decent (self hostable) program to orchestrate scripts, notify on failures, etc
Originally, I was using Jenkins (Yes, the CI pipeline java app) for this, but it was clear that is not its purpose, and it was fairly clunky. The only real upside was direct git support, but I don't mind incorporating git pulls into my jobs themselves.
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!
argo-cd - Declarative Continuous Deployment for Kubernetes
MLServer - An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
woodpecker - Woodpecker is a simple yet powerful CI/CD engine with great extensibility.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
drone - Gitness is an Open Source developer platform with Source Control management, Continuous Integration and Continuous Delivery. [Moved to: https://github.com/harness/gitness]
great_expectations - Always know what to expect from your data.
checks-api-plugin - Jenkins plugin that defines an API for Jenkins to publish checks to SCM platforms.
alibi-detect - Algorithms for outlier, adversarial and drift detection
Concourse - Concourse is a container-based continuous thing-doer written in Go.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
onedev - Git Server with CI/CD, Kanban, and Packages. Seamless integration. Unparalleled experience.