Situation
One of the world's largest shopping engagement platforms faced challenges in managing its growing Machine Learning (ML) initiatives. With multiple ML projects running simultaneously, data scientists relied on locally installed MLOps tools for experiment tracking, model registration, and asset versioning. This decentralized approach made collaboration difficult, as experiment results and assets (models and datasets) couldn’t be easily shared across teams.
To address these challenges, the client partnered with Intertec to build a centralized MLOps platform. The solution integrates backend and artifact stores, ensuring seamless data tracking, asset sharing, and efficient workflow management. By leveraging AWS cloud services, the new platform enables scalability, automation, and secure collaboration, improving overall ML productivity.
