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How AI performance management teams navigate the AI application development maze
As the world moves toward ubiquitous connectivity, everything -- devices, machines, cameras, and humans – is generating humongous data points like logs, audios, images, videos, and more.
Organizations are now analyzing these data points to extract intelligence and create a new range of services. Building intelligence involves analyzing these data sets for patterns with the help of technologies such as artificial intelligence, machine learning, and deep learning to create factories of the future, autonomous vehicles, smart and safe cities, smart farming and so on. Highlighted below are some of the key issues that AI stakeholders need to consider to accelerate their AI programs:
Data sets
Infrastructure and network
Algorithms and frameworks
AI deployment, model management and governance
Every AI application has its own complexity. It is good to have an AI Performance Management (AIPM) team that has the expertise to deal with the complexities of AI application development, build reference architectures, frameworks, tools, and understand governance procedures.
Image of Sridhar CV
Sridhar CV
Sridhar CV heads alliances and ecosystem partnerships for the incubation team for QC.
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Ravindran Subbiah
Ravindran Subbiah is an Entrepreneur-in-Residence (EIR) with the Operations Framework Incubation Program at TCS.
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Nitin Hanjankar
Nitin Hanjankar is the Presales, Marketing and Alliances Head for TCS Incubation with over three decades of industry experience.
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