O'Reilly on Data

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 362

Marketing Experimentation Metrics Testing 362

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 333

Management Machine Learning Experimentation Metrics 333

CIO Business Intelligence

MARCH 21, 2022

A data scientist’s chief responsibility is data analysis, which begins with data collection and ends with business decisions based on analytic results. The data that data scientists analyze draws from many sources, including structured, unstructured, or semi-structured data.

Unstructured Data 136

Unstructured Data Data Analytics Analytics Structured Data 136

Rocket-Powered Data Science

JULY 7, 2019

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: Cars, Trucks, Taxis. They cannot process language inputs generally.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. Instead, I built the AIgent. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

Domino Data Lab

OCTOBER 2, 2019

As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences. You can use a regression model. model = OLS(X['Y'], X[['D', 'intercept']]) result = model.fit() result.summary(). A complementary Domino project is available. . Introduction. Why did this work?

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Another way to find the metric you want to change is to look at your business model. The business model also tells you what the metric should be.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156