Darwin: Machine Learning Beyond Predefined Recipes
Guest contributorMachine LearningSponsored Postposted by SparkCognition April 23, 2019 SparkCognition
The same way a tailored suit feels and looks different from generic options because it actually fits, tailored models perform differently than pre-established boxed algorithms because they are custom-fitted to your data.
To answer this need, SparkCognition has developed Darwin™, a machine learning product that automates the building and deployment of models at scale. Darwin uses a patented approach based on neuroevolution that custom builds model architectures to ensure the best fit for the problem at hand.
Rather than simply choosing the best performer from a predefined list of algorithms, Darwin uses a blend of evolutionary and deep learning methods to iteratively find the most optimal model tailored to your data. This automated model building process effectively creates unique solutions that correctly and accurately generate predictions for your unique data problems.
How Does Darwin Work?
Darwin automates major steps in the data science process:
- Feature generation
- Construction of a supervised or unsupervised model
Darwin’s evolutionary process automatically generates thousands of models over the course of many generations, iterating on the best performers from each generation to create the next generation.
Want to know more? Come visit us at booth #319 at ODSC East 2019 this April 30 to May 3 to see a live demonstration, and read about Darwin’s unique process here.