Enhance Automated Model Building With the Latest Product
The version 2.0 of Darwin, an automated machine learning product considerably improves the user’s experience and capabilities in the platform by automating the model building process and accelerating the data preparation process with intuitive workflows.
FREMONT, CA: The global artificial intelligence (AI) company,SparkCognition, announced the release of version 2.0 of its automated machine learning (AutoML) product, Darwin. The new version considerably improves the user’s experience and capabilities in the platform by automating the model building process and accelerating the data preparation process with intuitive workflows.
SparkCognition offers solutions developed on Machine Learning (ML) techniques and patent-pending pattern recognition to protect data in vital scenarios. The company also deals with rising digital security threats as and when surfaced. “Humans can never keep up with the data complexity and software tools that enable IoT, such as sensors, network bus, and storage,” says Amir Husain, CEO of SparkCognition. “That’s how we position ourselves in the IoT stack - sitting on top of the sensor and data acquisition bus, making sense of the oceans of analog and digital big data streaming through, ultimately delivering valuable business-level insights to our clients,” Husain adds.
The enterprise is featured in “50 Most Promising Internet of Things Companies 2014” by CIO Review, Darwin version 2.0 brings powerful latest features to SparkCognition’s automated machine learning product.
AutoML is changing data science, as it automates the practice of building, deploying, testing, and maintaining models for a dataset. Darwin offers a spontaneous setting that takes consumers quickly from data to meaningful results. The outcomes facilitate organizations to scale the implementation of data science across teams, and the execution of ML applications across operations, becoming data-driven businesses.
Darwin v2.0 features comprise:
• Automated data quality checks problems in a dataset and presents solutions to any errors encountered.
• Enhanced neuro-evolutionary engine, which enables quickly-built custom models from scratch.
• Improved control over the model building process that conserves the performance and accuracy of models.
• Innovative genetic time series forecasting algorithms and sequence-to-sequence models competent in predicting multiple time horizons.
• Enhanced capability to capture compound relationships over time and make calculations using temporal convolution network (TCN) and long short-term memory (LSTM) architectures.
With the new and enhanced features, Darwin enables data scientists, software developers, SMEs, data, and business analysts to arrange their datasets and custom-build models from scrape much quicker than before. The models are additionally tailored to the given dataset by bearing in mind its complex relationships and intricacies, and swiftly assess model architectures to optimize from.
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