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OnLogic Partners With AI Development Specialists for Industrial AI Adoption

OnLogic is partnering with AI development and implementation specialists to help companies use AI-powered solutions, aiming to drive real business value. 

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Source: OnLogic

OnLogic has partnered with AI specialists Big Vision , Data Monsters and Mosaic Data Science to lend their experience to the company’s holistic industrial AI consulting and implementation services.

OnLogic says it works closely with customers to assess their business requirements and determine how to best leverage AI technologies. By partnering with other AI implementation specialists, OnLogic can provide customers with resources and solutions specifically tailored to their needs.

OnLogic AI advisors can assist users with leveraging machine learning, computer vision and conversational AI to improve the quality, efficiency, velocity and safety of their operations. Regardless of where a particular business is on their AI journey, OnLogic says it works to identify and implement the best application of AI for their desired outcomes, specifically tailored to their operations. Together with its partners, the OnLogic AI team can help customers with a full suite of AI deployment elements, including:

  • Identification of value-added AI use cases
  • Assessment and prioritization of AI implementations
  • Data architecture
  • Data collection, preparation and annotation
  • AI model selection and training
  • Edge computing architecture and hardware
  • Solution customization
  • Monitoring and maintenance.

According to OnLogic, its hardware can be used for everything from light AI inferencing to full-scale model building and training, due to a variety of available CPU options, internal GPU capabilities, discrete GPU offerings and specialized AI accelerators. By encouraging users to implement AI acceleration on their range of edge hardware, OnLogic intends to help unlock the potential to deploy AI computing power in remote locations and challenging environmental conditions. AI computing at the edge, or in concert with cloud resources, can help reduce latency and improve data security over fully cloud-based AI architectures.

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