mllint
MLflow
mllint | MLflow | |
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3 | 56 | |
72 | 17,284 | |
- | 1.3% | |
3.8 | 9.9 | |
almost 2 years ago | 6 days ago | |
Go | Python | |
GNU General Public License v3.0 only | 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.
mllint
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[D] How to maintain ML models?
Finally, there is the mllint tool that I have been developing during my MSc thesis on Software Quality in ML projects. While still a research prototype, it can already analyse your project and may be able to provide you with practical recommendations on what tools & techniques to employ for several aspects of your ML project's development. Feel free to try it out on your project and let me know what you think of it!
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Last week fluff-free AI, ML, and data-related original articles summary
- OpenAI released an improved version of Codex - Command-line utility to evaluate the technical quality of ML projects written in Python - It takes a whole convolutional neural network with five to eight layers to approximate a single cortical neuron - MLOps Monitoring Market Review
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[P][R] Announcing `mllint` — a linter for ML project software quality.
Source: https://github.com/bvobart/mllint
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?
mlnotify - 🔔 No need to keep checking your training - just one import line and you'll know the second it's done.
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
MLOps - MLOps examples
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
CortexTheseus - Cortex - AI on Blockchain, Official Golang implementation
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
guildai - Experiment tracking, ML developer tools
gorse - Gorse open source recommender system engine
dvc - 🦉 ML Experiments and Data Management with Git
awesome-seml - A curated list of articles that cover the software engineering best practices for building machine learning applications.
tensorflow - An Open Source Machine Learning Framework for Everyone