Accelerate data science.
Solve problems at scale.
"Automated model building is not the enemy of data scientists. We can use it as an ally in order to spend more time in more complex problems, converting data into knowledge."
"Technology that predicts the characteristics of semiconductor manufacturing equipment has been only for engineers who could develop robust models, but with the advent of Darwin, this will end, and virtual metrology technology will be democratized. Any engineer can get models that evolve automatically."
"Darwin is a catalyst for accelerating data science adoption. Its intuitive automation interface mimics workflows used by our traditional data science team, enabling other professionals in our organization to make use of the science."
"The automatic creation of evolutionary AI models by Darwin will definitely accelerate the data science process cycle. And with this great operability, Darwin will lead democratization of AI."
"With automated AI model building, we could shorten a project from a year to a month. This tool helps us to bring out business value faster."
The Darwin automated machine learning platform provides values for:
End-to-End Model Life Cycle
The Darwin platform automates the most time-consuming steps of the model life cycle ensuring the long term quality and scalability of models.
Uncover problems like missing data, low statistical variance, incorrect data types, and more.
Get suggestions to interactively fix problems in your dataset and make it usable for the model-building process.
Automate the custom creation of models based on Darwin’s patented neuroevolutionary approach.
Intuitively fine-tune training parameters to take your models to the next level and achieve the accuracy you need.
Extract insights and inform decisions through explainable model results that bubble up what matters in your dataset.
Break data silos with intuitive creation of data pipelines that feed new data into your deployed models.
Dynamically create model execution pipelines to obtain real-time predictions on incoming data.
Track the health of deployed models based on confidence of predictions to inform model maintenance tasks.
Factory of Use Cases
Identify customers at risk for loan defaults and delinquency.
Identify customers who are at risk of churning
Identify transactions at risk of being past due
Detect fraudulent activity on electronic transactions
Classify subterranean drill-head operational states
Predict automotive sub-component quality during assembly
Identify degradation in commercial aircraft components
Detect impurities during iron ore manufacturing
Accuracy of models for each scenario depends on the quality and relevancy of input datasets.