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
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