seldon-core VS zenml

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.

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?

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.

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/

Also note our github repo has a link to our slack where you can ask active users: https://github.com/SeldonIO/seldon-core

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 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.

Currently, you can see the integrations we support here and it includes a lot of tools in your list. I also feel I agree with your categorization (it is exactly the categorization we use in our docs pretty much). Perhaps one thing missing might be feature stores but that is a minor thing in the bigger picture.

GitHub: https://github.com/zenml-io/zenml

Hey everyone! At ZenML, we released today an integration that allows users to train and deploy models from pipelines in a simple way. I wanted to ask the community here whether the example we showcased makes sense in a real-world setting:

As of early March 2022 this is the new CI pipeline that we use here at ZenML and the feedback from my colleagues -- fellow engineers -- has been very positive overall. I am sure there will be tweaks, changes and refactorings in the future, but for now, this feels Zen.

ZenML is hiring for a Design Engineer.

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.

We’re looking for a Design Engineer with a multi-disciplinary skill-set who can take over the look and feel of the ZenML experience. ZenML is a tool designed for developers and we want to delight them from the moment they land on our web page, to after they start using it on their machines. We would like a consistent design experience across our many touchpoints (including the [landing page](https://zenml.io), the [docs](https://docs.zenml.io), the [blog](https://blog.zenml.io), the [podcast](https://podcast.zenml.io), our social media, the product itself which is a [python package](https://github.com/zenml-io/zenml) etc).

A lot of this job is about communicating complex ideas in a beautiful way. You could be a developer or a non-coding designer, full time or part-time, employee or freelance. We are not so picky about the exact nature of this role. If you feel like you are a visually creative designer, and are willing to get stuck in the details of technical topics like MLOps, we can’t wait to work with you!

Apply here: https://zenml.notion.site/Design-Engineer-m-f-1d1a219f18a341...

The best place to see MLflow Tracking and ZenML being used together in a simple use case is our example that showcases the integration. It builds on the quickstart example, but shows how you can add in MLflow to handle the tracking. In order to enable MLflow to track artifacts inside a particular step, all you need is to decorate the step with @enable_mlflow and then to specify what you want logged within the step. Here you can see how this is employed in a model training step that uses the autolog feature I mentioned above: