SparkCognition leverages its proprietary Automated Model Building solution to generate the most globally optimal model for each and every data set. Automated Model Building is capable of performing multiple analytical techniques, including Anomaly Detection, Clustering, Classification and Regression. SparkCognition’s product, SparkPredict™ is able to consume unlabeled data (or data without known failures and states, also known as unsupervised learning) and perform an ensembled, automated clustering technique.
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The Darwin Difference: Why Darwin Stands Out From the AutoML Pack
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 extraction of insights.
Use Case: A Full Framework for Automated Loan Processing
With AMB, lenders can expect an increase in loans offered, with optimized interest pricing and lower defaults, and a forecasted 20% ROI.
Customer Success Story: Predicting Customer Complaints for Telecom
With the power of NLP and auto ML a major telecom provider expects to reduce complaint call volume by 33%, all while increasing brand loyalty.
Use Case: Preventing Customer Churn for Retail Banks
As competition, banks are expected to lose revenue due to customer churn. Natural language processing and automated machine learning generate insights into why customers churn and how to retain them.
Use Case: Predicting Financial Market Regimes with AI
The ability to more accurately predict market volatility, price changes, and price change directions comes with AI. To find the best possible methodology, one investment and trading company staged an
Customer Success Story: Improving Refinery Safety and Efficiency with AI at the Edge
Leading contract manufacturer, Texmark Chemicals Inc., has historically monitored their equipment manually and at high cost. Facing increasing costs and additional safety liabilities from this...
Whitepaper: Neuroevolution Under the Hood
Neuroevolution amplifies a data scientist's work by enabling more effective model building, providing the ability to rapidly iterate through thousands of different models and architectures.
Use Case: Optimizing Energy Trading with Machine Learning
Utilities need to accurately forecast pricing for whole sales and retail markets to provide competitive offerings. Machine learning solutions allow utilities to move beyond the traditional approach.
Use Case: Improving Grid Reliability and Resiliency
The utility sector has already benefited from the use of machine learning by implementing predictive maintenance that predicts failures on critical equipment well in advance.
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AI is being adopted rapidly in the oil and gas industry due to the significant operational efficiencies it provides to operators through advanced analytics for predictive and prescriptive maintenance.
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From hedge fund managers to mutual funds and even private equity managers, alternative data has the power to improve valuation of securities and boost the clarity of the investment process.
Reimagine Offshore Maintenance with AI
The oil and gas industry is a dynamic, ever changing arena. SparkCognition has developed AI-based predictive and prescriptive maintenance solutions to help companies adapt to the age of digitization.
Customer Success Story: Optimizing Aerospace Maintenance with NLP
Customer Success Story: Identifying Vane Failure From Combustion Turbine Data
Combustion turbines are the central power-producing asset in a combined-cycle power plant. When a unique combustion turbine vane failure led to a two-month outage and $30M in lost opportunities...
Customer Success Story: Identifying Non-Productive Time and Invisible Lost Time on Oil Rigs
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Case Study: Resume Analysis Using NLP
Customer Success Story: KYC Process Automation for Banking
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