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

Ontotext

JULY 6, 2023

This shift of both a technical and an outcome mindset allows them to establish a centralized metadata hub for their data assets and effortlessly access information from diverse systems that previously had limited interaction. internal metadata, industry ontologies, etc.) names, locations, brands, industry codes, etc.)

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

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. Case Study 2: Circle of Friends. Why do they do it?

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

O'Reilly on Data

JULY 25, 2023

I also installed the latest VS Code (Visual Studio Code) with GitHub Copilot and the experimental Copilot Chat plugins, but I ended up not using them much. To me, this is a huge benefit of a conversational interface like ChatGPT versus an IDE autocomplete interface like GitHub Copilot, which doesn’t leave a trace of its interaction history.

Consulting 246

Consulting Interactive Software IT 246

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. One of the longer-term trends that we’re seeing with Airflow , and so on, is to externalize graph-based metadata and leverage it beyond the lifecycle of a single SQL query, making our workflows smarter and more robust. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

AWS Big Data

JULY 26, 2023

Using augmented ML search and generative AI with vector embeddings Organizations across all verticals are rapidly adopting generative AI for its ability to handle vast datasets, generate automated content, and provide interactive, human-like responses.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52