zenml VS engineering

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:

Slim.AI | Fullstack and Backend Engineers | REMOTE, international or Seattle/Bellevue/WA | Full-time | Golang, Node.js, Vue.js/Nuxt.js

I'm the founder and CTO at Slim.AI. We are a well funded seed stage startup (9M+) in the developer tooling space. Our mission is to simplify and accelerate the containerized app delivery (it's too hard, too complicated and with too much manual work). We are about to transition to the next phase and we are expanding our engineering team.

Our engineering team is the innovation engine for our product because we are building a solution to solve our own problems creating and running containerized cloud-native applications.

We use Golang, Node.js Serverless/Lambda and containers. We have frontend, backend and fullstack roles ( https://github.com/slim-ai/engineering ).

Our engineering principles:

* We use what we build.

Slim.AI | Backend and Fullstack Engineers | REMOTE, international or Seattle/Bellevue/WA | Full-time | https://github.com/slim-ai/engineering

We are a well funded seed stage startup (9M+) in the developer tooling space on a mission to redefine how DevOps is done for containerized apps (it's too hard, too complicated and with too much manual work). We are about to transition to the next phase and we are expanding our engineering team.

Our engineering team is the innovation engine for our product because we are building a solution to solve our own problems creating and running containerized cloud-native applications.

We use Golang, Node.js Serverless/Lambda and containers. Take a look at the backend ( https://github.com/slim-ai/engineering/blob/master/roles/bac... ) and fullstack ( https://github.com/slim-ai/engineering/blob/master/roles/ful... ) roles and our engineering principles to see if the role and how we do engineering looks interesting to you ( https://github.com/slim-ai/engineering#engineering-principle... ).

Email me at [email protected] if you'd like to learn more.

P.S.

And take a look at DockerSlim ( https://github.com/docker-slim/docker-slim ) if you are interested in working on the open source project that powers our SaaS.

Slim.AI | REMOTE or Seattle | Full-time | Developer Experience Lead | https://github.com/slim-ai/engineering

Do you enjoy working with lots of different applications stacks? Do you like helping others? Do you want to build lots of different applications? Are you interested in contributing to open source?

We are a funded seed stage startup in the developer tooling and DevOps space empowering developers to build and run their cloud-native applications. The current product is focusing on containers and the friction around them.

We are building a brand new engineering team. We are developer friendly, low on process with no mind-numbing bureaucracy or micromanagement. We are looking for people who'll be excited to be a part of the engineering team in an early stage startup during its inception phase building modern cloud-native applications the right way.

You can find out more about the mission, how we work and the roles here: https://github.com/slim-ai/engineering

Email me at [email protected] if you'd like to learn more.