IKEA engineers discovered the company had four personas that work with data and algorithms at different levels, each following different methods to productionize their AI/ML models. This presented a few engineering challenges. How could IKEA engineers: standardize all model development, handle the complexities surrounding Responsible AI, be more cost-efficient when developing compute-intensive models, address the needs of personas that are not code first, leverage open source components with custom-built components to produce a cohesive platform for MLOps, and establish the desired governance for such vast organization? The answer lay in establishing a custom-developed MLOps platform that leverages open source, public cloud, private cloud, and multiple interaction interfaces that meets the need of their data community. Learn from IKEA’s journey of building a MLOps platform with a platform-as-product mindset.
Resources:
How IKEA Retail Standardizes Docker Images for Efficient Machine Learning Model Deployment – https://www.docker.com/blog/how-ikea-retail-standardizes-docker-images-for-efficient-machine-learning-model-deployment/
Get started with Docker – https://www.docker.com/get-started/
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