White Papers

Go deep with technical resources on the most cutting-edge AI applications

  • From Data to Application: Darwin's Unique Approach to AutoML

    From Data to Application: Darwin's Unique Approach to AutoML

    Darwin automates time-consuming tasks ranging from model creation and optimization to model deployment and continuous maintenance.

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  • Case Study: Failure Detection in a Combustion Turbine

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  • Garbage In, Garbage Out: Automated ML Begins with Quality Data

    Garbage In, Garbage Out: Automated ML Begins with Quality Data

    Machine learning methods are highly dependent on the quality of the data they receive as input, but data preparation and cleaning can be an unwieldy task, taking up 60% of data scientists' time.

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  • Improving Offshore Production with AI-Based Predictive Analytics

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  • Breaking Away from Cookie-Cutter Algorithms: True Generalization with Evolutionary Methods

    Breaking Away from Cookie-Cutter Algorithms: True Generalization with Evolutionary Methods

    Most autoML solutions in the market focus on searching for the best algorithm to fit a given data set. However, these methods lack the ability to produce novel, elegant model architectures.

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  • Zero-Day Malware Prevention Report

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  • How to Put Machine Learning Models to Work: Bridging the Gap Between Model Production and Operationalization

    How to Put Machine Learning Models to Work: Bridging the Gap Between Model Production and Operationalization

    Automated machine learning has the potential to reduce the burden on overwhelmed teams by automating the bottlenecks in the data science process. But just having an algorithm isn't enough.

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  • eBook: Your Guide to Small Business Cybersecurity

    eBook: Your Guide to Small Business Cybersecurity

    Roughly two-thirds of Americans dream of opening a small business. However, an increasing number of small businesses face an unfortunate reality—becoming victims of a cyber attack.

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  • The Darwin Difference: Why Darwin Stands Out From the AutoML Pack

    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.

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  • eBook: AI Implentation in the Oil & Gas Industry

    eBook: AI Implentation in the Oil & Gas Industry

    Companies that emerge the strongest from inevitable slowdowns are the ones who adopt innovative technologies. This booklet highlights how AI implementation can help O&G companies in three key areas.

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  • Use Case: A Full Framework for Automated Loan Processing

    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.

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  • Customer Success Story: Predicting Customer Complaints for Telecom

    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.

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  • Use Case: Preventing Customer Churn for Retail Banks

    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.

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  • Use Case: Predicting Financial Market Regimes with AI

    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

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  • Customer Success Story: Improving Refinery Safety and Efficiency with AI at the Edge

    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...

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  • Whitepaper: Neuroevolution Under the Hood

    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.

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  • Use Case: Optimizing Energy Trading with Machine Learning

    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.

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  • Use Case: Improving Grid Reliability and Resiliency

    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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  • Use Case: Alternative Data for Investment Management

    Use Case: Alternative Data for Investment Management

    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.

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  • Reimagine Offshore Maintenance with AI

    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.

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  • Customer Success Story: Optimizing Aerospace Maintenance with NLP

    Customer Success Story: Optimizing Aerospace Maintenance with NLP

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  • Customer Success Story: Identifying Vane Failure From Combustion Turbine Data

    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...

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  • Customer Success Story: Identifying Non-Productive Time and Invisible Lost Time on Oil Rigs

    Customer Success Story: Identifying Non-Productive Time and Invisible Lost Time on Oil Rigs

    Non-productive time is a great enemy for oil and gas operators. E&P operators need to better categorize and analyze rig activities to track and eliminate non-productive time and invisible lost time.

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