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Operational AI: Realizing the next level of value from AI
Technology research firm Gartner estimates that 85% of all AI and ML projects do not benefit the business. One of the main reasons organizations struggle to get value from their AI projects is because they invested in data science to create innovative ML models and then effectively put them into production. failure to adopt the processes, teams, and tools of worth.
To avoid this trap and derive greater value from AI, companies must stop thinking of models as ends. A model is just a way of transforming data described in the form of a function — much like software. To move from “artisan” data science to “industrial” ML at scale, organizations need to adopt operational AI thinking along the following key dimensions:
- People: The ML Platform team responsible for managing the tools and processes to operationalize the model.
- Process: Standardized processes and best practices for putting models into production.
- Technology: An operational AI platform that specifically supports the requirements for operating, managing, and monitoring models in a production environment.
By adopting a software mindset about ML and implementing the teams, processes, and tools to deploy ML models safely and efficiently, enterprises can significantly increase the speed of AI and reduce the cost of ownership of MLOps. to improve data science productivity and deliver greater value from AI. innovation.
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What does it take to become an AI-advancing organization? Read our Guide to Operational AI to learn how to adopt operational AI thinking, including an operational AI roadmap, required talent and technology, and AI reference architectures.
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Get the Guide to Operational AI
What does it take to become an AI-advancing organization? Read our Guide to Operational AI to learn how to adopt operational AI thinking, including an operational AI roadmap, required talent and technology, and AI reference architectures.
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