MLflow VS zenml

Besides building, testing and deploying, Kraken CI is also a pretty nice tool to build an MLOps pipeline. In this article, it will be shown how to leverage Kraken CI to build a CI workflow for machine learning using MLflow.

For MLFlow users you can now serve models directly in MLFlow using MLServer and if you're a Kubernetes user you should definitely check out Seldon Core - an open source tool that deploys models to Kubernetes (it uses MLServer under the covers).

But as you can imagine, tracking each experiment with Git can become a hassle. We’d like to automate the logging process of each run. The same as for large file versioning, many tools emerged in recent years for experiment logging, such as W&B, MLflow, TensorBoard, and the list goes on. In this case, I believe that it doesn’t matter with which hammer you choose to hit the nail, as long as you punch it through.

Its a bug with mlflow -> https://github.com/mlflow/mlflow/issues/3755 Keep the server on, open another terminal export MLFLOW_TRACKING_URI env variable, if on windows set the env variable.....should work.

mlflow for logging and visualizing ML model experiments

So, if you even want to use MLFlow to track your experiments, run the pipeline on Airflow, and then deploy a model to a Neptune Model Registry, ZenML will facilitate this MLOps Stack for you. This decision can be made jointly by the data scientists and engineers. As ZenML is a framework, custom pieces of the puzzle can also be added here to accommodate legacy infrastructure.

Consideration Number #2: Consider using model life cycle development and management platforms like MLflow, DVC, Weights & Biases, or SageMaker Studio. And Ray, Ray Tune, Ray Train (formerly Ray SGD), PyTorch and TensorFlow for distributed, compute-intensive and deep learning ML workloads.

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:

ZenML | Developer Advocate | Full-time | Remote (Europe / UK) | [https://zenml.io](https://zenml.io)

Hey! We are an open-source company and the pulse of [ZenML](https://github.com/zenml-io/zenml)'s community is our driving force! ZenML is a MLOps framework to create reproducible ML pipelines for production machine learning use-cases.

As a Developer Advocate / 'Tech Evangelist', you will help us fulfil our mission by connecting with other developers, contributing to open-source, and sharing your knowledge and experience about ZenML and other leading technologies at conferences and meetups, in contributed articles, and on blogs, podcasts, and social media. Your work will foster a community inspired by ZenML and will drive our strategy around developer love and our participation in the open-source ecosystem. You will also be responsible measure engagement with the community, and find creative ways to drive it up.

We focus on generating awareness about ZenML by contributing to the ecosystem and enabling others to become evangelists outside the company as well. Not afraid to be hands-on, you might write sample code, author client libraries, provide insights to journalists, and work with strategic partners, users, and customers to excite and engage our developer communities.

For full details on this role, check out [https://zenml.io/careers/](https://zenml.io/careers/).

This spins up the infrastructure for you on a target of your choosing. In addition, ZenML takes care of deploying your pipelines to the relevant stack automatically. e.g. Try spinning up a Kubeflow-based stack (https://github.com/zenml-io/zenml/tree/main/examples/kubeflow) on your local machine with this simple command. ZenML will build the container for you, create the Kubeflow pipeline, and run it automatically, with a simple command. In the future, we hope to expand this to include more complex deployments.

ZenML is an open-source MLOps Pipeline Framework built specifically to address the problems above. Let’s break it down what a MLOps Pipeline Framework means: