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. As cybercriminals shift from targeting large corporations, your business is at risk of being in the crosshairs.
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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.
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.
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
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
Case Study: Predicting Maintenance and Production in Oil Wells
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.
DeepArmor Endpoint Protection Testing
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.
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.
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...
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
Maximizing Offshore Oil Well Production
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.
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.
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.
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.
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
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.
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...
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.
Case Study: Predicting Rare Failures in Hydro Turbines