nitroml
FLAML
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| nitroml | FLAML | |
|---|---|---|
| 1 | 9 | |
| 40 | 3,663 | |
| - | 3.0% | |
| 0.9 | 8.3 | |
| about 3 years ago | 12 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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nitroml
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Launch HN: MindsDB (YC W20) – Machine Learning Inside Your Database
The benchmarking challenges you are facing are pretty common in the AutoML community. My colleagues and I at Google Research are trying to solve this with https://github.com/google/nitroml. It's still super early days (no CI yet), but I think it could help your team benchmark on a set of open standard benchmark tasks as we open source more of the system.
FLAML
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AutoGen: Enabling Next-Gen GPT-X Applications
I really like the simplicity of this framework, and they hit on a lot of common problems found in other agent-based frameworks. Most intrigued by the RAG improvements.
Seems like Microsoft was frustrated with the pace of movement in this space and the shitty results of agents (which admittedly kept my interest turned away from agents for the last few months). I'm interested again because it makes practical sense, and from looking at the example notebooks, seems fairly easy to integrate into existing applications.
Maybe this is the 'low code' approach that might actually work, and bridge together engineering and non-engineering resources.
This example was what caught my eye: https://github.com/microsoft/FLAML/blob/main/notebook/autoge...
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
4. FLAML
- Show HN: AutoML Python Package for Tabular Data with Automatic Documentation
- [D] If there’s one practical tip you wish should have been drilled deeply into you when you first started out learning about deep learning, what would it be?
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what is the future of ML.NET?
Improved AutoML - Again, with collaboration from Microsoft Research, we used FLAML to update our existing AutoML solutions. What does this mean for you? You're using the latest techniques but all you need is a problem to solve and some data to get started.
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Automated Machine Learning (AutoML) - 9 Different Ways with Microsoft AI
For a complete tutorial, navigate to this Jupyter Notebook: https://github.com/microsoft/FLAML/blob/main/notebook/flaml_automl.ipynb
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[N] Fast AutoML with Microsoft's FLAML + Ray Tune
Microsoft Researchers have developed FLAML (Fast Lightweight AutoML) which can now utilize Ray Tune for distributed hyperparameter tuning to scale up FLAML’s resource-efficient & seamlessly parallelizable algorithms across a cluster.
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[R] FLAML - Fast and Lightweight AutoML library
Looks nice but I wonder if this is practical for non-tiny problems. The papers are a bit hard to follow but it looks like training is restarted with every new architecture choice. As for the library itself, the only large neural net example is a finetune of an NLP model that only searches over ADAM's optimizer params - which could be useful but it's a stretch to call that AutoML.
- Flaml – Cost-effective hyperparameter optimization AutoML
What are some alternatives?
lightwood - Lightwood is Legos for Machine Learning.
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
MindsDB - The platform for customizing AI from enterprise data
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.
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
FEDOT - Automated modeling and machine learning framework FEDOT
automl - Google Brain AutoML
question_generation - Neural question generation using transformers
autokeras - AutoML library for deep learning