Automated Machine Learning (AutoML) is an emerging field in which the process of building machine learning models to model data is automated.
Platform products for Machine Learning
Considerations for Deploying Machine Learning Models in Production
Here is a list of AutoML tools that I have found to be helpful to an AI/ML engineer.
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PyCaret
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auto-sklearn
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Auto-ViML
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AutoViz
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H2O
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MLFlow
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wandb
- MLFlow
Weights and biases (W&B/wandb) lets you track, compare, visualize, and optimize machine learning experiments with just a few lines of code.
wandb also lets you track your datasets.
Using W&B's lightweight tools, you can quickly track experiments, version and iterate on datasets, evaluate model performance, reproduce models, visualize results and spot regressions, and share findings with colleagues.
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auto-sklearn
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AutoGluon
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AutoKeras
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Auto-ViML
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SpeedML
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FLAML
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H2O
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PyCaret
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Hydra
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Kale
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MediaPipe
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Ray
- AutoViz
- Dataprep
Automated Machine Learning with H2O (towardsdatascience)
Auto-Sklearn for Automated Machine Learning in Python
Why AutoML Is An Essential New Tool For Data Scientists
AutoViz: A New Tool for Automated Visualization
SageMaker is a machine learning environment that simplifies the work of an ML developer by providing tools for extra fast model building and deployment.
In 2021, Amazon launched SageMaker Studio, the first integrated IDE for machine learning that provides a web interface to monitor all possible aspects of the life cycle of an ML model, basicslly Jupyter on steroids.
SageMaker is closely integrated into the AWS cloud and it also offers data labeling software and other features.
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PyCaret
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AutoGluon
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AutoKeras
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AutoTS
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Darts
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mlforecast
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TimeSynth
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TimeGAN
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Gretel.ai