DARWIN

Accelerate Data Science.
Solve Problems at Scale.

Darwin is a machine learning product that leverages the best of genetic algorithms and deep learning to provide the next step into the future of artificial intelligence.

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CREATE AND MANAGE THOUSANDS OF MODELS

Darwin enables organizations to build, deploy, and maintain all of their models from a single configuration-based environment that guides you every step of the way.

SCALE YOUR MODELS ACROSS OPERATIONS

Darwin streamlines model retuning and retraining tasks with intuitive approaches that enable scalable, maintainable ML applications across your organization.

CONTROL THE MODEL BUILDING PROCESS

Darwin exposes information on how models were generated, allowing full control of the process, 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 are performed as a single generation of Darwin’s evolutionary process, which contains hundreds of model architecture candidates.

DATA
INPUT

Generation 1

INPUT
OUTPUT

Generation 2

INPUT
OUTPUT

Generation N

INPUT
OUTPUT

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 are 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
OUTPUT

Generation 2

INPUT
OUTPUT

Generation N

INPUT
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.

Darwin provides value for:

Data Scientists

  • Accelerate the prototyping of use cases and refinement of models

  • Streamline the development and maintenance of thousands of models

  • Control every aspect of the process and integrate into your existing toolchain

Business Analysts

  • Effortlessly create models that directly act on your data

  • Accelerate the extraction of insights that drive business impact

  • Communicate findings with transparency through explainable AI

Executives

  • Efficiently drive AI innovation at scale across the entire organization

  • Augment and enable talent to focus on AI projects that impact the bottom line

  • 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.

-Jaime Castellanos, Global HITSS
Data Scientist

Unparalleled Performance

Darwin excels in time series data and complex problems

See the Darwin Efficacy Report for a complete view of these results.

Additional Resources

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