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ElectrifAi Announces Expansion of Machine Learning Model Offerings for Amazon SageMaker

  • Delivering fast and reliable machine learning business solutions

ElectrifAi, one of the world’s leading companies in practical artificial intelligence (AI) and pre-built machine learning (ML) models, announced expanded offerings of pre-built and pre-structured ML models for Amazon SageMaker, including models available from the Computer Vision and Image Analytics collections. Amazon SageMaker is a ML service from Amazon Web Services (AWS) that helps data scientists and developers to prepare, build, train and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML.

ElectrifAi has now published 58 machine learning models.

The new set of Computer Vision models can work on cameras, enabling new types of applications, and solving novel sets of challenges. For instance, cameras mounted to drones can be used to track people, vehicles and animals, even as the camera and item of interest moves rapidly. Audio analytics capabilities can also be added to enrich the overall solution.

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Examples of ElectrifAi’s Computer Vision use cases:

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  • Smart bird (drone) for perimeter control
  • Smart body camera for law enforcement
  • Field-ready image-based skin condition identification
  • Enhanced workplace safety
  • Identification of a specific person in a crowd
  • Animal welfare maintenance for farms

The Image Analytics models can detect, segment, and annotate medical anomalies with precision for Amazon SageMaker—allowing one to track the progress of a disease or the success or failure of a treatment protocol. The model can also identify a wide range of skin conditions almost instantly from a picture taken on a regular smartphone.

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Examples of ElectrifAi Image Analytics use cases:

  • Can be used at entry/exit points
  • Can be deployed anywhere with the aid of portable X-ray devices
  • Rapid tracking of individuals
  • Objective measurement of the effectiveness of treatment protocols over time
  • Can be used to help in visualizing internal anatomical features, disease identifier markers or other respiratory conditions

“Computer Vision on the edge is emerging as a game-changer in AI. We’re excited about how these new models will quickly enable new applications,” said Jim McGowan, ElectrifAi’s SVP of Cloud Partners and Head of Product. “These models hide all of the complexity behind a straightforward interface, simplifying the process of building all kinds of new tools.”

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