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.

Marketing 361

Marketing Experimentation Metrics Testing 361

Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. Continuous pipeline monitoring with SPC (statistical process control). Results (i.e.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before. Machine learning adds uncertainty.

Management 327

Management Machine Learning Experimentation Metrics 327

Occam's Razor

AUGUST 22, 2011

" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love! Web Data Quality: A 6 Step Process To Evolve Your Mental Model. "Engagement" Is Not A Metric, It's An Excuse. How to focus?"

KPI 124

KPI Metrics Measurement ROI 124

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.

Enterprise 353

Enterprise Modeling Risk Manufacturing 353

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Another pattern that I’ve seen in good PMs is that they’re very metric-driven.

Management 110

Management Machine Learning Experimentation Metrics 110