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  • 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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  • 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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  • Case Study: Failure Detection in a Combustion Turbine

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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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  • 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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  • 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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  • AV-Comparatives Malware Protection Test Fact Sheet

    AV-Comparatives Malware Protection Test Fact Sheet

    SparkCognition’s DeepArmor® Endpoint Protection Platform scored a 99.9% protection rate with zero false positives on common business applications.

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  • Case Study: Improving Offshore Production with AI-Based Predictive Analytics

    Case Study: Improving Offshore Production with AI-Based Predictive Analytics

    One major oil platform operator was facing recurring failures in production subcomponents that resulted in >10% downtime and millions of dollars of lost production. They employed SparkCognition’s turn

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  • Webinar: Are You Data-Ready for Predictive Maintenance in O&G?49:07

    Webinar: Are You Data-Ready for Predictive Maintenance in O&G?

    O&G companies' projects with machine learning-based predictive analytics will ultimately be driven by data quality, availability, and management. This webinar will expose you to the key data requireme

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  • Improving Offshore Production with AI-Based Predictive Analytics

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  • Case Study: Predicting Maintenance and Production in Oil Wells

    Case Study: Predicting Maintenance and Production in Oil Wells

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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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  • DeepArmor Endpoint Protection Testing

    DeepArmor Endpoint Protection Testing

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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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  • Automating Model Building on the Google Cloud Platform

    Automating Model Building on the Google Cloud Platform

    SparkCognition and Google Cloud Technology have partnered to weave artificial intelligence into the fabric of organizations by expanding the range of applications and tools optimized to run on GCP.

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  • Case Study: Improving Operational Efficiency and Returns in Oil and Gas

    Case Study: Improving Operational Efficiency and Returns in Oil and Gas

    To remain competitive and maximize returns, oil and gas companies must utilize the massive amounts of data generated in the fields. The rise of the Internet of Things and smart sensors have created...

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  • The Practical Guide to Industrial AI: Benefits and Implementation

    The Practical Guide to Industrial AI: Benefits and Implementation

    Artificial intelligence is certainly not a new topic. The field has seen starts and stalls, as many of AI's successes have been promising for research progress. With new developments and impactful

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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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  • Darwin Efficacy Report

    Darwin Efficacy Report

    Darwin's automated model building capabilities offer unparalleled performance to generate highly accurate models. Thus, we sought to compare how Darwin performs against other platforms on the market.

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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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  • Predicting Customer Insurance Costs with Artificial Intelligence2:59

    Predicting Customer Insurance Costs with Artificial Intelligence

    Predict customer insurance costs using machine learning. Darwin builds neuroevolution tailored models that reduce the amount of tie spent building and maintaining models. Darwin. Automated Model

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