TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant.
TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.
Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there.
1. Write Your First AI Project in 15 Minutes
2. Generating neural speech synthesis voice acting using xVASynth
3. Top 5 Artificial Intelligence (AI) Trends for 2021
4. Why You’re Using Spotify Wrong
FeatureTools is an open-source python library for automated feature engineering.
Feature tools automatically create features from temporal and relational datasets.
Deep Feature Synthesis
Featuretools uses DFS for automated feature engineering. You can combine your raw data with what you know about your data to build meaningful features for machine learning and predictive modeling.
Precise Handling of Time
Featuretools provides APIs to ensure only valid data is used for calculations, keeping your feature vectors safe from common label leakage problems. You can specify prediction times row-by-row.
Reusable Feature Primitives
Featuretools comes with a library of low-level functions that can be stacked to create features. You can build and share your own custom primitives to be reused on any dataset.
Auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
Automated Machine Learning in four lines of code
cls = autosklearn.classification.AutoSklearnClassifier()
predictions = cls.predict(X_test)
Auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning, and ensemble construction.
Auto-sklearn 2.0 works the same way as regular auto-sklearn.
Onnxruntims is a cross-platform, high performance ML inferencing and training accelerator.
It is compatible with many popular ML/DNN frameworks, including PyTorch, TensorFlow/Keras, scikit-learn, and more.
Many users can benefit from ONNX Runtime, including those looking to:
- Improve inference performance for a wide variety of ML models
- Reduce time and cost of training large models
- Train in Python but deploy into a C#/C++/Java app
- Run on different hardware and operating systems
- Support models created in several different frameworks
Using INNXRUNTIME you can:
- Speed up machine learning process
- Plug into your existing technology stack
- Build using proven technology
Skorch is a scikit-learn compatible neural network library that wraps PyTorch. Skorch requires Python 3.5 or higher. Skorch is the spiritual successor to nolearn, but instead of using Lasagne and Theano, it uses PyTorch.
Skorch does not re-invent the wheel, instead getting as much out of your way as possible. If you are familiar with sklearn and PyTorch, you don’t have to learn any new concepts, and the syntax should be well known
Skorch also provides many convenient features, among others: