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 363

Marketing Experimentation Metrics Testing 363

CIO Business Intelligence

MARCH 21, 2022

According to data from Robert Half’s 2021 Technology and IT Salary Guide, the average salary for data scientists, based on experience, breaks down as follows: 25th percentile: $109,000 50th percentile: $129,000 75th percentile: $156,500 95th percentile: $185,750 Data scientist responsibilities.

Unstructured Data 133

Unstructured Data Data Analytics Analytics Structured Data 133

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: (1) Retail. (2) 2) Disaster Response. (3) 3) Machine maintenance. (4)

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

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 339

Management Machine Learning Experimentation Metrics 339

Insight

MARCH 12, 2020

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. The latter is any type of external data that has been attached to a book? Instead, I built the AIgent. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

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. Remember that the raw number is not the only important part, we would also measure statistical significance. Circle of Friends had all of this information.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Domino Data Lab

OCTOBER 2, 2019

As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences. What you’re seeing is a general rule: when two variables share a common cause, they will be correlated (or, more generally, statistically dependent) even when there’s no causal relationship between them.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68