Machine Learning

Monitoring Machine Learning Accuracy in the Enterprise

Machine learning tools and practices continue to develop at a dizzying pace. The industry has moved on from running Python tools on the data scientist’s local laptop to hosted services from all the major cloud vendors that…

Monitoring Machine Learning Accuracy

The biggest problem in machine learning model accuracy is that all models degrade. They perform well initially but as the real world changes the models no longer capture the underlying structure. Both concept drift (the meaning and universe of values of the target…

AI Is Eating Software:

“Automation of Automated Machine Learning” Will Replacing Programming in Applications from the Inside Out

How Second Generation AutoML Will Replace Software Development from the Inside Out

AutoML — Not Just for Experts Anymore

The genesis of automated machine learning was from experts who spent weeks on a prediction or classification problem trying various algorithms and hyperparameters until an optimal result was find. Instead of manually selecting each prediction algorithm and…

Of Grapes and Mushrooms: From AutoML to OptiML

Something I rarely tell people is that I am currently still nominally a PhD candidate at UC Davis’ viticulture program (yes I’m a dropout…

So How Does Auger.AI Provide the Fastest, Most Accurate AutoML?

In our last post we pointed out that Auger outperforms the new Microsoft AutoML and other AutoML tools such as H2O and TPOT. Specifically…

Comparing open source AutoML tools

Auger vs. H2O vs. TPOT on sample datasets

We often get asked how Auger compares to other AutoML tools. Luckily in these days of open source tools this is possible to do in a way that can be validated and reproduced…