Tag: scaling ML initiatives

Concerned about accelerating AI workflows, addressing multiple use cases, and scaling ML initiatives? Here are the rules to help you succeed. In this two-part blog series, we review the ten rules that will ensure success with your first AI/ML project paving the way for many more. In the first part, we discussed the alignment of business objectives with use cases, getting a head start on data preparation and the mechanics of feature engineering. We also talked about understanding AutoML tools’ capabilities and ensuring the right modeling approach while balancing model accuracy and interpretability. This second part will discuss why visibility to the ML processes and results are critical, the importance of data science education, real-time analytics, infrastructure compatibility, and ML operationalization. Here are the five best practices: Ensure the ML project has visibility. ML initiatives fail because they operate with a silo mentality, like a secret science experiment that no…