Machine Learning in Energy & Natural Resources

Learn how the best companies are improving ROI, managing risk, and making groundbreaking new discoveries by leveraging state-of-the-art AI and machine learning systems.

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AI in Energy & Natural Resources

Helping transform organizations with AI technology. AI is helping companies optimize the entire value chain and gain a distinct advantage over other operators in a an intensely competitive market. Armed with large amounts of data and AI, innovative companies are tackling the greatest challenges in the industry head-on. In addition to core activities like exploration and production, companies are leveraging AI to manage capital planning and other corporate and back-office activities, making AI-driven decisions part of the fabric of all operations.

Discover the benefits of AI in energy and natural resources.

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Challenges

Insufficient access to scalable compute resources

Gaining access to compute resources and orchestrating workloads requires extensive experience in tooling that becomes a costly distraction for data scientists and ML engineers. Infrastructure bottlenecks reduce velocity and precision and increase model ops friction, time to market, and operational risk.

Lack of standardization, process, & centralized hub

Collaboration among distributed research teams without a unified tool is a liability. Workflows that lack a standardized process and a unified hub lead to re-work and hinders the ability for data scientists to find, understand, build-on, and contribute to the various models in R+D and production.

Consumed by menial & redundant tasks

Data scientists and machine learning engineers spend roughly 25% of their time developing models. This means that 75% of their time is spent on costly distractions related to tooling and infrastructure. A end-to-end ML pipeline enables a rapid model delivery cycle without the need to perform cumbersome routine tasks.

Gradient can help

Conduct AI without bottlenecks

Experiment faster and deliver more breakthroughs. Remove DevOps pain with independent access to compute. Use the right tools for the job. Share and repurpose work across the team. More easily get models from development into production.

Build a modern AI practice

Create an organizational capability out of machine learning. Gain visibility into work happening across the organization. Accelerate the data machine learning management lifecycle. Reduce regulatory and operational risk while maintaining and updating models more frequently, with greater precision.

Become your unified AI hub

Support data science without sacrificing governance or security. Future-proof your machine learning stack. Gain transparency with complete model reproducibility. Offer a centralized platform for collaboration. Reduce model ops friction for rapid model delivery and iteration.

AI Applications

High Value Use Cases in Energy & Natural Resources

  • Forecast volume with incomplete data
  • Predictive Maintenance
  • Quality control optimization
  • Demand forecasting
  • Manage and optimize supply chains
  • Forecast business needs
  • Identify root causes of downtime
  • Analyze price trends

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