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

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

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation.

Metrics 141

Metrics Reporting Analytics Marketing 141

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

Occam's Razor

JULY 18, 2011

The only requirement is that your mental model (and indeed, company culture) should be solidly rooted in permission marketing. You just have to have the right mental model (see Seth Godin above) and you have to… wait for it… wait for it… measure everything you do! Just to ensure you are executing against your right mental model.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Another pattern that I’ve seen in good PMs is that they’re very metric-driven.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

AUGUST 13, 2012

Yehoshua I've covered this topic in detail in this blog post: Multi-Channel Attribution: Definitions, Models and a Reality Check. I explain three different models (Online to Store, Across Multiple Devices, Across Digital Channels) and for each I've highlighted: 1. That means: All of these metrics are off.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Occam's Razor

DECEMBER 12, 2016

You got me, I am ignoring all the data layer and custom stuff! But, at the end of the day presence of a Tag Manager communicates to me that the company is serious about data collection and data quality. Digital Marketing & Measurement Model. All that is great. That's it. Problematic, right? Management.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Cloudera

JUNE 6, 2019

This duplication can become such a management nightmare that they may prefer to return to the original model and simply live with noisy neighbors. Furthermore, both Compute Cluster 1 and Compute Cluster 2 can operate on the same copy of the data with a single set of security policies. A Better Approach: Virtual Private Clusters.

Metadata 85

Metadata Data Lake Optimization Strategy 85

Occam's Razor

MARCH 23, 2010

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. Because every tool uses its own sweet metrics definitions, cookie rules, session start and end rules and so much more. Likely not. Omniture cannot save you. Only you can save yourself.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Occam's Razor

OCTOBER 19, 2010

Move from a data collection obsession and develop a crush on data analysys. Mongoose Metrics ~ ifbyphone. I know Mongoose Metrics a bit more and have been impressed with their solution and evolution over the last couple of years. Experimentation and Testing Tools [The "Why" – Part 1]. Three tools.

Analytics 135

Analytics Testing Measurement Optimization 135

Occam's Razor

MAY 27, 2015

It is an investment in numerous report writers or data (puking) automation or hiring a small army in India or Philippines to do that, before investing in any smart Analyst. It is being hyper-conservative when it comes to creativity and experimentation because of quant-issues. < /A > ]. Cookies are all we need!

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

datapine

JANUARY 31, 2022

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Occam's Razor

JANUARY 7, 2014

The organization functions off a clearly defined Digital Marketing & Measurement Model. #1. Remember none of these jobs will do any data collection/IT work, even in medium-sized companies.) All standard reports are simply the vendor engineer's attempt to showcase the data in the tool. " Kisses. Time on Site!

Data-driven 140

Data-driven Consulting Reporting Marketing 140

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation. And for good reason!

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

APRIL 29, 2013

Marketer, is not spent with data you''ll fail to achieve professional success.]. Many used some data, but they unfortunately used silly data strategies/metrics. And silly simply because as soon as the strategy/success metric being obsessed about was mentioned, it was clear they would fail. You'll get fired.

Marketing 166

Marketing Metrics Advertising Measurement 166