graphql-ws VS zenml

When using a GraphQL over Websocket protocol you can execute all operation kinds (query, mutation and subscription) over that transport as it is a bidirectional protocol.

The GraphQL over WebSocket solution definitely requires an additional library on the client for the complexity of the protocol (namely graphql-ws). The readme of that project describes how you can connect to a GraphQL over WebSocket compliant server.

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There is already a JavaScript implementation for server + client with support for multiplexing from the same maintainer of graphql-ws: graphql-sse

Actually WebSocket is not the only protocol for executing GraphQL subscriptions. There is also a graphql-over-sse protocol, which is much more lightweight, and uses Server Sent Events. A popular implementation of it is the graphql-sse library, which is maintained by the same person as graphql-ws. The setup and API of both libraries is pretty similar.

That said, I feel so close to success that I can't quite give up. I am using graphql-ws as I understand this to be the new standard implementation of GraphQL Subscriptions. However, of course, Apollo subscriptions have not been updated to implement this. So, looking at the Recipes over on the graphql-ws documentation I have found the guide for using Apollo client. However as I am new to Apollo, it is using a lot of terms I am unfamiliar with.

Okay but seriously, if you've fallen down the rabbit hole of Apollo docs pointing you towards one library (subscription-transport-ws) which then points you to another (graphql-ws) , and so on and so forth, then hopefully this helps pull you out.

The most common protocol used today for subscriptions is the Apollo websocket protocol.

GraphQL subscriptions are just web sockets. You can check graphql-ws package if you want something without Apollo dependency. Though I don't understand why you want to not depend on it in the first place.

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: