Revolutionizing QC in Manufacturing Pipelines with AI


Artificial intelligence (AI) is finding its way into use cases from marketing to manufacturing. It leaves no doubt that we are in the early stages of the next industrial revolution.

Given this reality, if you haven’t already started to craft an AI strategy, you will need to soon.

But the good news is you’re not falling behind yet. Most organizations are just beginning their AI journey. That said, don’t wait. You need to craft a robust data strategy that leverages AI architectures to drive innovation across all business units.

At SFL Scientific we’ve seen an increased demand for our data strategy, scientific advisory, and AI development and deployment services. In the manufacturing industry, we work with our clients to drive operational costs lower through enhancements in production line efficiency. We do this by reducing the prevalence of defects and anomalies as far upstream in the production process as possible, cutting waste and scrap material generation, optimizing product distribution, and associated logistics.

Learn more about how to leverage AI workflows to transform quality control (QC) processes for manufacturing production lines in our joint webinar with Cray “Automating QC Through Computer Vision and Deep Learning.”

With the advances being made through the Internet of Things (IoT), we now have a fantastic opportunity to ingest these varied data streams and unlock unrealized insights. The natural evolution of these IoT systems is building out an AI-powered Analytics of Things (AoT) architecture.

Integrating AI into enterprise operations is no longer a “moonshot” initiative thanks to the rise of NVIDIA GPU-driven supercomputer architectures like the Cray® CS-Storm™ accelerated cluster supercomputer. Leveraging these architectures has made adoption of machine learning (ML) and deep learning (DL) workflows very much a reality, both from a cost standpoint as well as a time standpoint.

Developing and training AI algorithms no longer takes years. In some cases, it can be accomplished in just weeks, even days. Implementing a production-ready image classification workflow tasked for identification and sorting to automate the anomaly and defect detection in the QC process becomes a viable use case in today’s emerging technology market and one that SFL Scientific has delivered many times over.

In our webinar, you’ll get a deeper understanding of AI and how you can apply it to the automation of defect and anomaly detection in manufacturing. Also, we’ll discuss how to take a programmatic approach when building out large-scale data pipelines and how to design and implement these architectures using on-premises scale-out platform architectures based on the Cray supercomputing system. Sign up for the on-demand webinar here.

About SFL Scientific
SFL Scientific is an award-winning artificial intelligence firm based in Boston, MA. Focused on helping clients digitally transform and innovate by leveraging their data. We provided specialized consulting services in data strategy, scientific advisory as well as full lifecycle development of end to end AI architectures.

Topic: Revolutionizing manufacturing quality control with AI
When: On Demand

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