The is the final entry in a five-part insideHPC series that takes an in-depth look at how machine learning, deep learning and AI are being used in the energy industry. Read on for help determining where and how to adopt machine learning technology in your business.
Download the full report.
Determining where in a complex workflow to adopt new tech, like machine learning technology, can be a daunting task. A simple high-level checklist should include the following steps:
- Map out the workflows that are already computationally intensive.
- Determine if previous information from the different sub-workflows is well-understood and can be used to train an ML system.
- Investigate different algorithms and perform proof-of-concept projects to see if an ML system can be trained with previous data.
- Consult with experts to understand whether specific algorithms can be tuned to make decisions faster (i.e. less precise arithmetic) based on the task at hand.
- Look at whether the same systems already being used for HPC-type applications can be repurposed or combined with ML systems in order to make fuller use of the resources. This would include resource management systems to allocate workloads.
The combination of HPC and AI systems into a cluster that can adapt to the needs of a workload is an extremely valuable asset to any organization involved in energy exploration and the process of bringing that energy to market. While simulations of underground structures are critical in reducing the cost of drilling, just as important is an understanding of the process of bringing that energy to market.
Determining where in a complex workflow to adopt new tech, like machine learning technology, can be a daunting task.
By using HPC, AI systems and machine learning technology to accurately locate energy and to be confident that the entire system is working at peak efficiency (no leaks), organizations can benefit from the expertise in system design and delivery that organizations like Dell EMC and NVIDIA bring to market.
Organizations can benefit from the expertise in system design and delivery that organizations like Dell EMC and NVIDIA bring to market.
With the explosion of the use ML, there are resources that can help you learn more about how to take advantage of these exciting new technologies:
- Dell EMC HPC Community is a worldwide technical forum that fosters the exchange of ideas among researchers, computer scientists, technologists and engineers, and promotes the advancement of innovative, powerful HPC solutions.
- NVIDIA Deep Learning Institute (DLI), offering hands-on training for developers, data scientists, and researchers looking to solve the world’s most challenging problems with deep learning using the latest GPU-accelerated deep learning platforms.
This insideHPC Guide series also previously covered the following topics:
- Opportunities Abound: HPC and Machine Learning for Energy Exploration
- Energy Companies Embrace Deep Learning for Inspections, Exploration & More
- Edge Computing Proves Critical for Drilling Rigs, Pipeline Integrity
- Computing Environments Options for Energy Exploration and Monitoring
Download the full report, “Machine Learning in Energy: A Hot Spot in Seismic Processing,” courtesy of Dell EMC and Nvidia, to learn more about how machine learning is being used to move the energy industry forward.
