Remember when "requires Docker to run" felt intimidating?
I guess Iāve been there too. But fast-forward a decade, and now even my dev environment runs in a container. Why? Because speed, consistency, and simplicity matter.
After 9 months of development, we are releasing getML 1.5 into the world. This is whats new.
It's a classic, around since the 70s, used it myself back in uni, but data science needs have clearly evolved.
With TSFresh, Blue Yonder's engineering team set the sails for brute-force feature engineering, combining a large set of 100+ predefined feature templates with traditional feature selection methods. This approach performs well, but their implementations limitations: computational complexity on real-world data and limited support for multivariate time series.
It's a classic, around since the 70s, used it myself back in uni, but data science needs have clearly evolved.
With TSFresh, Blue Yonder's engineering team set the sails for brute-force feature engineering, combining a large set of 100+ predefined feature templates with traditional feature selection methods. This approach performs well, but their implementations limitations: computational complexity on real-world data and limited support for multivariate time series.
