O'Reilly on Data

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ).

Marketing 362

Marketing Experimentation Metrics Testing 362

Rocket-Powered Data Science

JULY 7, 2019

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). A reference to a new phase in the Industrial Revolution that focuses heavily on interconnectivity, automation, Machine Learning, and real-time data. They cannot process language inputs generally.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Data Science 101

JANUARY 15, 2020

Bias ( syatematic unfairness in data collection ) can be a potential problem in experiments and we need to take it into account while designing experiments. Some pitfalls of this type of experimentation include: Suppose an experiment is performed to observe the relationship between the snack habit of a person while watching TV.

Experimentation 96

Experimentation Statistics Measurement Testing 96

Data Science and Beyond

APRIL 5, 2021

The initial covid-19 lockdown provided me with extra free time to make the measurement and offsetting of Automattic’s emissions from data centre power use happen. I previously posted about my experiences with RLS offline data collection and visualisation of the collected data , and have since helped with quite a few RLS surveys.

Experimentation 61

Experimentation Testing Publishing Measurement 61

Cloudera

FEBRUARY 16, 2022

blueberry spacing) is a measure of the model’s interpretability. Each project consists of a declarative series of steps or operations that define the data science workflow. We can think of model lineage as the specific combination of data and transformations on that data that create a model. Model Visibility.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

Insight

MARCH 12, 2020

With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Instead, I built the AIgent.

Modeling 59

Modeling Metadata Publishing Sales 59

O'Reilly on Data

MAY 14, 2020

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

Management 363

Management Experimentation B2B Machine Learning 363