neptune-contrib
MLflow
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| neptune-contrib | MLflow | |
|---|---|---|
| - | 54 | |
| 27 | 17,185 | |
| - | 2.1% | |
| 0.0 | 9.9 | |
| over 1 year ago | 6 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
neptune-contrib
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Tracking mentions began in Dec 2020.
MLflow
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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.
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[Q] Is there a tool to keep track of my ML experiments?
Hi, you should have a look at ML flow https://mlflow.org or weight and biases https://wandb.ai/site
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Looking for recommendations to monitor / detect data drifts over time
Dumb question, how does this lib compare to other libs like MLFlow, https://mlflow.org/?
What are some alternatives?
scikit-learn - scikit-learn: machine learning in Python
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
tensorflow - An Open Source Machine Learning Framework for Everyone
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
Keras - Deep Learning for humans
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
bodywork - ML pipeline orchestration and model deployments on Kubernetes.
guildai - Experiment tracking, ML developer tools
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
dvc - 🦉 ML Experiments and Data Management with Git
TFLearn - Deep learning library featuring a higher-level API for TensorFlow.