Session Outline
At Bolt, we want scientists and engineers to solve customer’s problems, not infrastructure ones. That’s why we’ve been investing into centralized machine learning infrastructure for 3 years, and it helped us scale 50+ of projects, making billions of real-time inferences weekly. In this talk, I will share how we’ve built abstraction over Machine Learning infrastructure, enabling teams to move fast, while platforming teams are developing value adding features and adopting them easily.
Key Takeaways
– Even though there are many “tools for the job” in the data and ML space, leaving choice of them to product teams creates duplication of components and expensive tech debt.
– We believe that for a common problem there should be a common reusable solution, providing smart defaults.
– With good abstraction over data and ML pipelines data infrastructure team easily migrated users to better solutions (SaaS, Cloud or internal ones), optimised costs globally across the company and solved issues for 50+ ML projects on the platform with a single release.
– Centralized platform allowed Bolt ML platform team to build and integrated value-added components such as data monitoring and no-code smoke/load with negligible cost to client teams.
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