Production ML: 6 Key Challenges & Insights—an MLOps Roundtable Discussion

Deploying machine learning (ML) models into production poses several challenges, including establishing efficient data pipelines, understanding and attributing costs, and designing organizational processes that support quick execution. A roundtable discussion with ML experts highlighted six key takeaways: bringing ML models into production requires coordinating data, tools, and teams; demonstrating the return on investment of ML is crucial to leadership's continued investment, but calculating ROI without good cost attribution is challenging; as organizations grow larger, their challenges deploying ML into production also grow due to increased complexity and requirements; setting up a data science team for success involves generating reliable and accessible training data and providing them with an environment to experiment and train models; juggling different data processing strategies can be tricky, requiring pairing batch and streaming infrastructure with tools like Tecton; and future-proofing systems and processes is crucial to mitigate the pain of scaling ML.