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5 Answers
Deepak Mukunthu
Deepak Mukunthu
Salesforce Senior Director of Product, Einstein AIJune 29
While working on ML product/feature, there are 2 sets of metrics: 1. Product success metrics that product managers define. Purpose of the is to measure the business/product outcome you are trying to optimize for. Your standard metrics like customer adoption, usage, retention, satisfaction etc. f......Read More
348 Views
Suhas Manangi
Suhas Manangi
Airbnb Group Product ManagerJune 7
In addition to core business metrics that are improtant for a product success, below are the additional ones AI PMs obsess over to ensure the success upon launch doesn't regress over time. 1. Precision and Recall 2. False Positive 3. Model quality monitoring metrics based on where is the risk......Read More
615 Views
Rapha Danilo
Rapha Danilo
Gong Director of ProductApril 28
The same first principles actually still apply to AI PMs, but with an added dimension of complexity, which is that a generational paradigm/platform shift like AI requires a PM to re-think the benchmarks for what good looks like, consider the new types of outputs/inputs required, and the additiona......Read More
436 Views
Savita Kini
Savita Kini
Cisco Director of Product Management, Speech and Video AIMarch 4
Metrics are an interesting question. This really depends on the type of product we are building that leverages ML. Since ML can be use for example in electronic records, sales workflows, computer vision type use cases or speech / audio use cases some of which I am familiar with -- we can break it......Read More
290 Views
Savita Kini
Savita Kini
Cisco Director of Product Management, Speech and Video AIMay 10
There is no one "metric" that ML Product Teams will use to define success. It entirely depends on what is the "ML" used in the context of a feature or product. Typical metrics might include * Quality improvements achieved via ML implementation vs traditional algorithmic implementations * U......Read More
156 Views