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@nixtlainc started last year as a side project
Today we reached 1 million downloads🎉
Our goal is to shake the time series industry and make state-of-the-art algorithms available for everyone 🔥
This is how we got there
🧵1/11
#reinventforecasting
Today we reached 1 million downloads🎉
Our goal is to shake the time series industry and make state-of-the-art algorithms available for everyone 🔥
This is how we got there
🧵1/11
#reinventforecasting
@nixtlainc's ecosystem consists (now) of 5 #python libraries
Focus: speed, scalability, and accuracy 🚀
Some features:
* @scikit_learn syntax
* Native support for #spark, @raydistributed, and @dask_dev
* Models! Eg #arima, #LightGBM, #NeuralNetworks, #transformers 🤖
2/11
Focus: speed, scalability, and accuracy 🚀
Some features:
* @scikit_learn syntax
* Native support for #spark, @raydistributed, and @dask_dev
* Models! Eg #arima, #LightGBM, #NeuralNetworks, #transformers 🤖
2/11
We are really proud of the open-source adoption
Repos from Amazon, Mozilla, and DataBricks use us as dependencies 🏄♂️
We have contributions from people working at H20, Microsoft, Google, Facebook, SalesForce, Oracle, Shopify, AT&T, Blueyonder, Stanford, MIT and UCL 🙏
3/11
Repos from Amazon, Mozilla, and DataBricks use us as dependencies 🏄♂️
We have contributions from people working at H20, Microsoft, Google, Facebook, SalesForce, Oracle, Shopify, AT&T, Blueyonder, Stanford, MIT and UCL 🙏
3/11
Connaissez-vous #pyCaret ?
Si ce n'est pas le cas, je vous conseille vivement d'y jeter un oeil
Cette bibliothèque vous permet de tester facilement les performances des principaux algorithmes de Machine Learning
illustration >
Si ce n'est pas le cas, je vous conseille vivement d'y jeter un oeil
Cette bibliothèque vous permet de tester facilement les performances des principaux algorithmes de Machine Learning
illustration >
Test sur la classification du dataset IRIS
on obtient ces résultats :
