zenml VS sourcegraph

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:

Sourcegraph | REMOTE | Full-Time | Machine Learning Engineer, Developer Advocate, Enterprise Product Manager, Technical Advisor | https://sourcegraph.com

Sourcegraph is a code AI platform that makes it easy to read, write, and fix code–even in big, complex codebases.

We are building Cody, an AI coding assistant that uses code search and code intelligence to help devs quickly understand what's happening in code and generate new code that matches the best practices in your codebase. Cody supports AI-enabled autocompletion, fixing bugs, refactoring, test generation, code explanation, and answering high-level questions. You can read Steve Yegge's post on why Cody's code context engine differentiates it from the fast-moving field of AI dev tools: https://about.sourcegraph.com/blog/cheating-is-all-you-need.

Apply here: https://grnh.se/0572f98b4us

That's pretty much what https://sourcegraph.com/ are selling, is it not?

Despite their shitty rug-pull <https://github.com/sourcegraph/sourcegraph/pull/53345>, I do really like Sourcegraph and one doesn't (currently?!) need to be logged in to use it: https://sourcegraph.com/search and they have a handy rewrite pattern such that one can just plug the repo path into the URL for quick searching e.g. https://sourcegraph.com/github.com/JetBrains/intellij-commun...

- https://sourcegraph.com is pivoting and building a copilot application (named Cody). This is pretty good, since sourcegraph is great at understanding your code

While a readable Dockerfile can work as documentation, there are a few caveats:

* the application needs to be designed to work outside containers (so, no hardcoded URLs, ports, or paths). Also, not directly related to containers, but it's nice if it can be easily compiled in most environments and not just on the base image.

* I still need a way to notify me of updates; if the Dockerfile just wgets a binary, this doesn't help me.

* The Dockerfiles need to be easy to find. Sourcegraph's don't seem to be referenced from the documentation, I had to look through their Github repos to find https://github.com/sourcegraph/sourcegraph/tree/main/docker-... (though most are bazel scripts instead of Dockerfiles, but serve the same purpose)

We use Sourcegraph, which is a tool that searches through code in repositories. We leverage this tool in order to understand the adoption curve of our components across all of Reddit. We have a dashboard for each of the platforms to compare the inclusion of RPL components over legacy components. These insights are helpful for us to make informed decisions on how we continue to drive RPL adoption. We love seeing the green line go up and the red line go down!

SourceGraph: https://github.com/sourcegraph/sourcegraph/pulls?q=is%3Apr+a...