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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
3 projects | dev.to | 6 Jun 2023
Options for configuration of python libraries - Stack Overflow
2 projects | /r/learnpython | 14 May 2023
In search for a tool that needs comparable configuration I looked into mlflow and found this. https://github.com/mlflow/mlflow/blob/master/mlflow/environment_variables.py There they define a class _EnvironmentVariable and create many objects out of it, for any variable they need. The get method of this class is in principle a decorated os.getenv. Maybe that is something I can take as orientation.
[D] Is there a tool to keep track of my ML experiments?
2 projects | /r/MachineLearning | 13 May 2023
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
Looking for recommendations to monitor / detect data drifts over time
3 projects | /r/datascience | 15 Apr 2023
Dumb question, how does this lib compare to other libs like MLFlow, https://mlflow.org/?
Integrating Hugging Face Transformers & DagsHub
2 projects | /r/mlops | 27 Mar 2023
While Transformers already includes integration with MLflow, users still have to provide their own MLflow server, either locally or on a Cloud provider. And that can be a bit of a pain.
Any MLOps platform you use?
5 projects | /r/selfhosted | 25 Feb 2023
I have an old labmate who uses a similar setup with MLFlow and can endorse it.3 projects | /r/learnmachinelearning | 25 Feb 2023
MLflow - an open-source platform for managing your ML lifecycle. What’s great is that they also support popular Python libraries like TensorFlow, PyTorch, scikit-learn, and R.
Selfhosted chatGPT with local contente
3 projects | /r/selfhosted | 24 Feb 2023
even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.
ML experiment tracking with DagsHub, MLFlow, and DVC
4 projects | dev.to | 12 Jan 2023
Here, we’ll implement the experimentation workflow using DagsHub, Google Colab, MLflow, and data version control (DVC). We’ll focus on how to do this without diving deep into the technicalities of building or designing a workbench from scratch. Going that route might increase the complexity involved, especially if you are in the early stages of understanding ML workflows, just working on a small project, or trying to implement a proof of concept.
AI in DevOps?
2 projects | /r/devops | 6 Dec 2022
What are some open-source ML pipeline managers that are easy to use?
7 projects | /r/mlops | 3 May 2023
[P] I reviewed 50+ open-source MLOps tools. Here’s the result
3 projects | /r/MachineLearning | 29 May 2022
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.
[D] Feedback on a worked Continuous Deployment Example (CI/CD/CT)
2 projects | /r/MachineLearning | 12 Apr 2022
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:
How we made our integration tests delightful by optimizing our GitHub Actions workflow
3 projects | dev.to | 11 Mar 2022
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.
Ask HN: Who is hiring? (March 2022)
30 projects | news.ycombinator.com | 1 Mar 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...
Ask HN: Who is hiring? (January 2022)
28 projects | news.ycombinator.com | 3 Jan 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/).
Taking on the ML pipeline challenge: why data scientists need to own their ML workflows in production
4 projects | dev.to | 6 Dec 2021
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:
Spot the difference in ML costs
2 projects | dev.to | 6 Dec 2021
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 :
10 Ways To Level Up Your Testing with Python
2 projects | dev.to | 10 Nov 2021
There's nothing like working on testing to get you familiar with a codebase. I've been working on adding back in some testing to the ZenML codebase this past couple of weeks and as a relatively new employee here, it has been a really useful way to dive into how things work under the hood.
Show HN: Deploy ML Models on a Budget
6 projects | news.ycombinator.com | 31 Jan 2021
What are some alternatives?
clearml - ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
guildai - Experiment tracking, ML developer tools
dvc - 🦉 Data Version Control | Git for Data & Models | ML Experiments Management
tensorflow - An Open Source Machine Learning Framework for Everyone
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
neptune-client - :ledger: Experiment tracking tool and model registry
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
gensim - Topic Modelling for Humans
dagster - An orchestration platform for the development, production, and observation of data assets.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows