IT@Intel: Autonomous Quality in AI Model Productization: A Journey

September 20th, 2022 |
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IT Best Practices: Intel IT’s artificial intelligence (AI) group works across Intel to transform critical work, optimize processes, eliminate scalability bottlenecks and generate significant business value (more than USD 1.5B return on investment in 2021). Our efforts unlock the power of data to make Intel’s business processes smarter, faster and more innovative, from product design to manufacturing to sales and pricing.

Our machine learning operations (MLOps) methodology requires embedding quality-control principles into the heart of the AI model productization process to ensure the scalability of our industrialization of the AI model production pipeline. Our principles provide many advantages, including the ability to achieve the following:
• Apply quality principles to different projects by abstracting the quality-control strategy elements.
• Quickly implement specific quality controls with reusable building blocks and shared components (this task used to take weeks).
• Minimize the cost and effort required to maintain the hundreds of AI models in production by adhering to systematic quality-control metrics.

Our dedication to improve autonomous quality in AI model productization led us to develop Microraptor, a set of MLOps capabilities that enable this operation at scale. MLOps is the practice of efficiently developing, testing, deploying and maintaining ML in production.

For more information on Intel IT Best Practices, please visit

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Posted in: Artificial Intelligence, Intel, Intel IT, IT White Papers, IT@Intel