What do leading-edge CDOs already in ML production say works best? Download this report to find out.
Machine learning (ML) success depends on maintaining a holistic understanding of the ML lifecycle, to ensure collaboration and smooth handoffs from data engineering to data science to ML engineering, and make decisions with the end-goal in mind.
In this eBook, we share a hands-on playbook for helping leaders of data science teams as well as ML practitioners build an effective, scalable ML production process.
Built based on learnings from client deployments, this eBook answers key questions like
No need to replace your data platform or change the popular tools your data scientists use to build models.