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. You might have millions of short videos , with user ratings and limited metadata about the creators or content. Machine learning adds uncertainty.

Management 332

Management Machine Learning Experimentation Metrics 332

Insight

MARCH 12, 2020

Data Collection The AIgent leverages book synopses and book metadata. To my knowledge, the most extensive repository of synopses and metadata is Goodreads. To collect these genre tags and other metadata, I took advantage of the well-documented Goodreads API. features) and metadata (i.e. In other words, if 0.1%

Modeling 59

Modeling Metadata Publishing Sales 59

Occam's Razor

APRIL 8, 2013

The word hypothesis means a lot of different things, but in this context I like this definition from Wikipedia the best: People refer to a trial solution to a problem as a hypothesis, often called an "educated guess”, because it provides a suggested solution based on the evidence. The result? The graph is impressive, right?

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

AWS Big Data

JULY 3, 2023

Major market indexes, such as S&P 500, are subject to periodic inclusions and exclusions for reasons beyond the scope of this post (for an example, refer to CoStar Group, Invitation Homes Set to Join S&P 500; Others to Join S&P 100, S&P MidCap 400, and S&P SmallCap 600 ). Load the dataset into Amazon S3.

Snapshot 84

Snapshot Data Lake Testing Strategy 84

O'Reilly on Data

MARCH 23, 2023

There’s a very important difference between these two almost identical sentences: in the first, “it” refers to the cup. In the second, “it” refers to the pitcher. And it can look up an author and make statistical observations about their interests. She poured water from the pitcher to the cup until it was empty.

IT 262

IT Modeling Testing Risk 262