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Introduction to MLOps at the Edge

Digital transformation stories always start from a clear need that could not be solved so far and a technological solution that remedies it. An almost universal premise that is reproduced over the years.

With the exponential development in hardware processing capacity and the commoditization of artificial intelligence models, the edge has emerged as an alternative to the cloud for secure and private real-time AI deployment.

Critical sectors such as energy and industry have strongly embraced this technology. However, there are great challenges for its management: applications and algorithms have much shorter life cycles than traditional industrial systems (with useful lives of up to 20 years), so it is necessary for the industry to adapt to these cycles and be able to deploy and maintain them remotely, updating and retraining models to scale projects in a managed, secure and controlled way.

Alejandro Cantos is an Electronics Engineer with a Master’s degree in Satellite Technology from the Polytechnic University of Madrid. He is currently VP of Product and Design at Barbara, i.e. the voice of the customer within the company.

Barbara is the Industrial Edge Platform for AI/ML teams to deploy ultra-fast secure models on any hardware.

He has over 15 years of experience leading development and product teams in industrial environments such as avionics, defense and security, payment systems, railways, and industrial robotics. He is also a professor of technology and innovation at IE Business School.