SparkCognition's Darwin™ is an automated machine learning (AutoML) product that empowers you with the right set of tools to quickly prototype use cases and achieve results faster than traditional data science methods. Darwin accelerates data science at scale, enabling you to assess the quality of your dataset and advising you on how to fix problems to make it suitable for the model-building process. Darwin then automates time-consuming tasks that range from model creation and optimization to model deployment and continuous maintenance. Darwin uses a patented neuroevolutionary approach to custom-build models that perfectly fit your data, resulting in superior levels of accuracy and performance. Through this blend of evolutionary algorithms and deep learning methods, Darwin accelerates the iterative improvement of models while allowing different degrees of control for customization.
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- Expedite Data Preparation Tasks
Darwin proactively uncovers problems within your dataset and interactively provides advice on how to address them, ensuring your data is ready for the automated model-building process.
- Create and Manage Thousands of Models
Darwin enables data science and analytics teams to build, deploy, and maintain all of their models at scale from a single productive environment that fully integrates with your existing toolchain through SDK and API options.
- Control the Whole Process from Data to Model
Darwin allows complete governance over methods used to prepare your data and selection of optimization functions, helping you influence the results you want while preserving the accuracy and performance of models.
- Unparalleled Accuracy Through Deep Learning
Darwin's neuroevolutionary process specializes in the search and auto-tuning of neural architectures based on the intricacies of your data, defining and exposing every aspect of the network and resulting in more accurate, sophisticated models.
- Handle Complex Temporal Relationships
Darwin uses long short-term memory (LSTM) and temporal convolutional network (TCN) architectures to capture complex relationships over time and exploit them to make more accurate predictions.
- True Generalization to Address the Unknown
Darwin's neuroevolutionary process is based on a fitness function. It quickly adapts to changes in upcoming data to achieve maximum accuracy under dynamic circumstances, evolving to maintain complexity, efficacy, and efficiency.