- Published on December 29, 2020
- In Deep Tech
Rethinking The Way We Do Machine Learning: An Engineering Perspective
- By Ram Sagar
Modern large-scale automation systems integrate thousands to hundreds of data points. Demands for more flexible reconfiguration of production systems and optimisation across different information models, standards and legacy systems challenge current system interoperability concepts. According to experts, this has become an increasingly important problem that needs to be addressed to fulfil these demands in a cost-efficient manner under constraints of human capacity and resources concerning timing requirements and system complexity.
Understanding Interoperability
AI is used in multiple ways from providing intelligent recommendations for shopping to detecting harmful content to translating text and generating automated captions. The "behind the scenes" version of these applications ty
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Ram Sagar
I have a master's degree in Robotics and I write about machine learning advancements.
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