Data Quality Deep Learning Experimentation Metrics 
O'Reilly on Data
JULY 28, 2020
The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.
O'Reilly on Data
MARCH 31, 2020
Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Even if a product is feasible, that’s not the same as product-market fit.
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O'Reilly on Data
APRIL 19, 2021
The biggest problems in this year’s survey are lack of skilled people and difficulty in hiring (19%) and data quality (18%). The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%). Bad data yields bad results at scale. Techniques.
DataKitchen
APRIL 13, 2021
RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Data breaks.
Domino Data Lab
JUNE 23, 2019
Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics.
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