Gradient Boosting Machine (GBM) modeling is a powerful machine learning technique for advanced root cause analysis in manufacturing. It will uncover problems that would be missed by regression-based statistical modelling techniques and single tree methods, but can easily be used by analysts with no expertise in statistics and modelling to solve complex problems. It is an excellent choice for advanced equipment commonality analysis and will detect interactions between process factors (for example, machines, recipes, process dates) that are responsible for bad product. It can also be used to identify complex nonlinear relationships and interactions between product quality measurements (for example, yield, defects, field returns) and upstream measurements from the product, process, equipment, component, material, or environment.
Published By: Dell EMC
Published Date: Nov 01, 2019
Workstations are often crucial for development and use of manufactured machines. Voxeleron is a case in point. The healthcare-focused company turned to the Dell Tower Workstation, capable of housing two Intel® Xeon® CPUs, three NVIDIA Quadro GV100 GPUs, lots of memory, and large storage capacity, and optimized to accelerate AI analysis of 3D imaging and deep-learning research from the scans of the optical coherence tomography (OCT) machines.
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Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled. Neural networks are one type of model for machine learning; they have been around
Machine learning is proving its power across virtually every industry in ways that add actionable insight and efficiency. But one can look at the rise of this transformative paradigm with a more focused lens to see AI technologies as a business tool of the highest order, one that improves processes and inspires new models. AI, in other words, has a big role to play on the balance sheet.
Two leading brands in very different spaces — Capital One in financial services, John Deere in agriculture — are seeing efforts that stretch back decades come to fruition with the launch of cloud-based AI platforms. Capital One is developing digital products and experiences using machine learning to help millions of customers with their financial lives; John Deere’s Precision Agriculture solution helps farmers gain precise information about their machines and crops. In both instances, AI and a cloud platform combine to enable transformation.
Artificial intelligence (AI) leads the charge in the current
wave of digital transformation underway at many global
companies. Organizations large and small are actively
expanding their AI footprints as executives try to comprehend
more fully what AI is and how they can use it to capitalize
on business opportunities by gaining insight to the data
they collect that enables them to engage with customers
and hone a competitive edge. But, while AI may indeed be
the frontier of enterprise technology, there remain many
misconceptions about it.
Part of the confusion stems from the fact that AI is an
umbrella term that covers a range of technologies —
including machine learning, computer vision, natural language
processing, deep learning, and more — that are in various
stages of development and deployment. The use of AI for
dynamic pricing and targeted marketing has been in use for
a while, but actual AI computing where machines think like
humans is still many years from becoming mainstream. T