The playbook that these consumer internet companies use to build their AI systems — where a single one-size-fits-all AI system can serve massive numbers of users — won’t work for these other industries (manufacturing, agriculture & healthcare).
To bridge this gap and unleash AI’s full potential, executives in all industries should adopt a new, data-centric approach to building AI.
The data-centric approach to AI, supported by tools designed for building, deploying, and maintaining AI applications — called machine learning operations (MLOps) platforms — will make this possible.
In a time of great AI talent shortage, a data-centric approach allows many subject matter experts who have vast knowledge of their respective industries to contribute to the AI system development.
The shift toward data-centric AI development is being enabled by the emerging field of MLOps, which provides tools to design AI systems easier than ever before.
Ensuring high-quality data means that AI systems will be able to learn from the smaller datasets available in most industries.
For example, even while building a POC system, urge the teams to begin developing a longer-term plan for data management, deployment, and AI system monitoring and maintenance.
A new data-centric mindset, coupled with MLOps tools that allow industry/business domain experts to participate in the creation, deployment and maintenance of AI systems, will ensure that all industries can reap the rewards that AI can offer.
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