DARWIN™
Accelerate Data Science.
Solve Problems at Scale.
Darwin is an automated model building product that allows you to go from data to model in less time than traditional methods, enabling the rapid prototyping of scenarios and productive extraction of insights.
Darwin successfully predicted 86% of degradation in cooling units on aircraft for a multinational airplane manufacturer.
Darwin accurately routed 75% of support tickets to the proper group for a major exploration and production operator.
Darwin identified 90% of factors influencing quality in the manufacturing process for an automotive OEM.
MAXIMIZE THE POTENTIAL OF YOUR DATA TO SOLVE PROBLEMS
Darwin's intuitive environment helps you better understand how to use your data for machine learning, uncovering potential issues and advising you on the best approaches to fix them.
CREATE AND MANAGE THOUSANDS OF MODELS
Darwin enables you to build, deploy, and maintain all of your models at scale from a single, intuitive environment that guides you every step of the way.
CONTROL THE WHOLE PROCESS FROM DATA TO MODEL
Darwin provides complete governance over methods used to prepare your data and optimize models, including SDK and API options that facilitate integration with existing systems.
How Darwin Works
Darwin uses a patented approach based on neuroevolution that custom builds model architectures to ensure the best fit for the problem at hand.
Darwin automates three major steps in the data science process:
DATA CLEANING
Data sets are automatically converted into a usable form for algorithmic development.
FEATURE GENERATION
Darwin automatically generates dozens of features from the data to drive toward a better solution.
MODEL BUILDING
Through neuroevolution, Darwin builds and optimizes either a supervised or unsupervised model.
THE EVOLUTION OF A NEURAL NETWORK MODEL
Each of these steps is performed as a single generation of Darwin’s evolutionary process, which contains hundreds of model architecture candidates.
DATA
INPUT
Generation 1
Generation 2
Generation N
MODEL
OUTPUT
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 optimal model tailored to your data. This automated model building process effectively creates unique solutions that precisely and accurately generate predictions for your data problems.
Each of these steps is performed as a single generation of Darwin’s evolutionary process, which contains hundreds of model architecture candidates.
DATA
INPUT
Generation 1
Generation 2
Generation N
MODEL
OUTPUT
THE EVOLUTION OF A NEURAL NETWORK MODEL
Generation 1
INPUT
Generation 2
INPUT
Generation N
INPUT
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 optimal model tailored to your data. This automated model building process effectively creates unique solutions that precisely and accurately generate predictions for your data problems.
Darwin provides value for:
Data Scientists
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Accelerate the prototyping of use cases and refinement of models
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Streamline the development and maintenance of thousands of models
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Control every aspect of the process and integrate into your existing toolchain
Business Analysts
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Effortlessly create models that directly act on your data
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Accelerate the extraction of insights that drive business impact
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Communicate findings with transparency through explainable AI
Executives
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Efficiently drive AI innovation at scale across the entire organization
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Augment and enable talent to focus on AI projects that impact the bottom line
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Meet demand and time to market goals without sacrificing opportunity
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.
Unparalleled Performance
Darwin excels in time series data and complex problems
See the Darwin Efficacy Report for a complete view of these results.