Best practices in building and operating an ML platform
Constructing an enterprise ML platform is a multifaceted undertaking. It often requires significant time, with organizations taking six months or more to implement the initial phase of their ML platform. Continuous efforts are needed to incorporate new functionalities and enhancements for many years to come. Onboarding users and ML projects onto the new platform is another demanding aspect, involving extensive education for the user base and providing direct technical support.
In some cases, platform adjustments might be necessary to ensure smooth onboarding and successful utilization. Having collaborated with many customers in building their enterprise ML platform, I have identified some best practices for the construction and adoption of an ML platform.
ML platform project execution best practices
- Assemble cross-functional teams: Bring together data engineers, ML researchers, DevOps engineers, application...
