Zenml Alternatives
Similar projects and alternatives to zenml
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SonarQube
Static code analysis for 29 languages.. Your projects are multi-language. So is SonarQube analysis. Find Bugs, Vulnerabilities, Security Hotspots, and Code Smells so you can release quality code every time. Get started analyzing your projects today for free.
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onnxruntime
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seldon-core
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Scout APM
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zotero
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serverless-graphql
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huggingface_hub
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Hasura
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Mattermost
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n8n
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zenml reviews and mentions
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[P] I reviewed 50+ open-source MLOps tools. Here’s the result
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.
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[P] ZenML: Build vendor-agnostic, production-ready MLOps pipelines
GitHub: https://github.com/zenml-io/zenml
- Show HN: ZenML – Portable, production-ready MLOps pipelines
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[D] Feedback on a worked Continuous Deployment Example (CI/CD/CT)
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:
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How we made our integration tests delightful by optimizing our GitHub Actions workflow
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.
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Ask HN: Who is hiring? (March 2022)
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...
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How to improve your experimentation workflows with MLflow Tracking and ZenML
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 helps data scientists work across the full stack
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Ask HN: Who is hiring? (January 2022)
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/).
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[P] ZenML: An extensible, open-source framework to create reproducible machine learning pipelines
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: An extensible, open-source MLOps framework to create production-ready machine learning pipelines.
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Taking on the ML pipeline challenge: why data scientists need to own their ML workflows in production
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:
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Spot the difference in ML costs
If you're looking for a head start for spot instance training, check out ZenML, an open-source MLOps framework for reproducible machine learning. Running spot pipeline in ZenML, is as easy as :
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Why deep learning development in production is (still) broken
Our attempt to solve these problems is ZenML, an extensible, open-source MLOps framework. We recently launched and are now looking for practitioners to solve their problems in production use-cases! So, head over to GitHub, and don't forget to leave us a star if you like what you see!
Stats
zenml-io/zenml is an open source project licensed under Apache License 2.0 which is an OSI approved license.
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