zenml VS proposals

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:

Check out temporal.io open source project. It was built at Uber for large scale business-level processes. So any data pipelines are low-rate use cases by definition.

With KuFlow it is also possible to work with serverless workflows apart from Temporal.io, we explain it in this blog entry, but in summary, almost as a no-code tool, the correct use It would be a rather low-code tool; in just a matter of minutes with our drag-and-drop tool, you can have a workflow that interacts with one or more users of the organization.

Otherwise you may want to look into Kafka or Fluvio to ensure that task runs at least once. If you're doing something like batch operations as a background task, Temporal is another great option.

The runtime is developed using Temporal, which is one of the main tools that we are currently using at KuFlow. Thanks to, all the workflow executions are robust: your application will be durable, reliable, and scalable.

Hmmm I got confused by the name. I thought it's related to https://temporal.io/

Have you looked at temporal.io open source platform? It uses event sourcing as an implementation detail. But it greatly simplifies the user experience compared to "raw event sourcing."

Rewrite Airflow on top of temporal.io. This way, you get unlimited scalability and very high reliability out of the box and would be able to innovate on the features that matter for DE.

Hi all, founder @ Retool here. Over the past year, we’ve been working on Retool Workflows; a fast way for engineers to automate tasks with code. We started building the product because we ourselves (as developers) were looking for something in-between writing cron jobs (which involves a lot of boilerplate) and Zapier (which oftentimes isn’t customizable enough, since it doesn’t _really_ support writing code).

Workflows is a code-first automation tool: you’re _expected_ to write code, but we handle all the boilerplate for you. For example: out-of-the-box integration with 80+ resources (you probably don’t want to be trying to figure out OAuth 2.0 with Salesforce!), monitoring and observability (so you can see the output of every run in the past, and immediately be notified if something goes wrong), and permissions (e.g. some Okta groups can see the outputs of Workflows, but can’t change the code itself).

Right now, the product is cloud-only, but we’re hard at work at an on-prem, self-hosted version (in a Docker image). If you’re interested in that version, feel free to email us at [email protected]. We aim to get it out in the next few weeks. Self-hosted Retool is responsible for a large portion of our usage today, and we’re excited to be supporting Workflows too.

All Retool plans now include 1GB of Workflows throughput, which we think is quite generous (80% of active Workflows users are below 1GB). We don’t bill by run at all, so you’re welcome to run as many workflows as you want.

We use a bunch of interesting technology for Workflows; we are, for example, using Temporal (https://temporal.io/) under the hood. That’s something we’re going to be writing a blog post about later. (We’ve been hard at work on the launch, hah.)

In such a diverse world, it would be boring to have a single way of doing things. That's why at KuFlow we support different ways to implement the logic of our processes and tasks. And in this post, we will talk about one of them, the orchestration through Temporal, which gives us a powerful way to manage our workflows.