paradigm
omegaml
paradigm | omegaml | |
---|---|---|
9 | 2 | |
36 | 94 | |
- | - | |
7.6 | 8.1 | |
11 months ago | 8 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
paradigm
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Deploying speech recognition models at scale
I built Paradigm from scratch to deploy any model at scale. It deploys the model on Kubernetes with load balancers. If you run into any issues, I'm happy to guide you on how to use it.
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Which is the best MLOps tool for getting started?
I started with paradigm. I got a deeper understanding about argo workflows through it as well. Helps to get a proper grab of industry standards from the beginning.
- What are some open-source ML pipeline managers that are easy to use?
- I use this OS tool to deploy LLMs on Kubernetes.
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Serving Scikit-Learn model on EC2 instance and Scaling
For scalability, it should be on Kubernetes. This is the best solution I have come across. You can deploy the model as a service with a LoadBalancer. You can refer to Kubernetes services or use a tool such as this one that handles building the service for you.
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Who wants to run ML pipelines on Kubernetes? This might be the simplest tool for the job.
I came across this tool today and checked it out, I feel this can get the job done very quickly without so many complex features. It is also very small in size, so does not take up a lot of space in the cluster as well.
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[P] I found the simplest tool to run ML pipelines on Kubernetes. Github link in comments.
Link - https://github.com/ParadigmAI/paradigm It seems to be a pretty new project. But this has high usability.
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Airflow + Slurm for ML Training Pipelines?
Prefect is a good choice, But I wanted a much simpler tool. Hence, I built a barebone workflow controller here.
omegaml
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What are some open-source ML pipeline managers that are easy to use?
May I add, https://github.com/omegaml/omegaml
- Who uses Apache Airflow for MLOps? Enlighten me.
What are some alternatives?
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
flecs - A fast entity component system (ECS) for C & C++
srez - Image super-resolution through deep learning
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
MLflow - Open source platform for the machine learning lifecycle
aws-sfn-resume-from-any-state - Resume failed state machines midstream and skip all previously succeded steps.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
dagster - An orchestration platform for the development, production, and observation of data assets.
Metrics - Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave