nuclio
MLflow
nuclio | MLflow | |
---|---|---|
4 | 56 | |
5,159 | 17,284 | |
0.6% | 1.3% | |
9.4 | 9.9 | |
about 24 hours ago | 5 days ago | |
Go | 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.
nuclio
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
For the efficiency of "serverless" functions, I would consider Nuclio as a viable option to rely on.
- Deploying Python Script as ML Service
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Appwrite, the open-source Firebase alternative releases v0.13
Does Appwrite offer any benefits over Nuclio?
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Azure function alternative
And surfing the web i've found this: https://github.com/nuclio/nuclio
MLflow
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Observations on MLOps–A Fragmented Mosaic of Mismatched Expectations
How can this be? The current state of practice in AI/ML work requires adaptivity, which is uncommon in classical computational fields. There are myriad tools that capture the work across the many instances of the AI/ML lifecycle. The idea that any one tool could sufficiently capture the dynamic work is unrealistic. Take, for example, an experiment tracking tool like W&B or MLFlow; some form of experiment tracking is necessary in typical model training lifecycles. Such a tool requires some notion of a dataset. However, a tool focusing on experiment tracking is orthogonal to the needs of analyzing model performance at the data sample level, which is critical to understanding the failure modes of models. The way one does this depends on the type of data and the AI/ML task at hand. In other words, MLOps is inherently an intricate mosaic, as the capabilities and best practices of AI/ML work evolve.
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My Favorite DevTools to Build AI/ML Applications!
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It includes features for experiment tracking, model versioning, and deployment, enabling developers to track and compare experiments, package models into reproducible runs, and manage model deployment across multiple environments.
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
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EL5: Difference between OpenLLM, LangChain, MLFlow
MLFlow - http://mlflow.org
- Explain me how websites like Dall-E, chatgpt, thispersondoesntexit process the user data so quickly
- [D] What licensed software do you use for machine learning experimentation tracking?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
MLflow:
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Options for configuration of python libraries - Stack Overflow
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.
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[D] Is there a tool to keep track of my ML experiments?
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.
What are some alternatives?
OpenFaaS - OpenFaaS - Serverless Functions Made Simple
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
faasd - A lightweight & portable faas engine
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
fission - Fast and Simple Serverless Functions for Kubernetes
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
fn - The container native, cloud agnostic serverless platform.
guildai - Experiment tracking, ML developer tools
kompose - Convert Compose to Kubernetes
dvc - 🦉 ML Experiments and Data Management with Git
faas-netes - Serverless Functions For Kubernetes
tensorflow - An Open Source Machine Learning Framework for Everyone