Cloudera

FEBRUARY 16, 2022

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. classification problem. Figure 01: Is this a chihuahua or a muffin?

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

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

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?—?for finished his first novel, Cuckoo’s Calling.

Modeling 59

Modeling Metadata Publishing Sales 59

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

Occam's Razor

JULY 18, 2011

You must use metrics that are unique to the medium. Ready for the best email marketing campaign metrics? So for our email campaign analysis let’s look at metrics using that framework. Optimal Acquisition Email Metrics. Allow me to rush and point out that this metric is usually just directionally accurate.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

Rocket-Powered Data Science

JULY 6, 2021

In at least one way, it was not different, and that was in the continued development of innovations that are inspired by data. This steady march of data-driven innovation has been a consistent characteristic of each year for at least the past decade. 2) MLOps became the expected norm in machine learning and data science projects.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

Occam's Razor

JUNE 6, 2007

Bonus: Interactive CD: Contains six podcasts, one video, two web analytics metrics definitions documents and five insightful powerpoint presentations. Experimentation & Testing (A/B, Multivariate, you name it). I am thrilled to say that my book Web Analytics: An Hour A Day has been published and is now widely available.

Analytics 60

Analytics Metrics Marketing Dashboards 60

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. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.

Management 110

Management Machine Learning Experimentation Metrics 110

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

Data quality plays a role into this. 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. You'll be surprised, there's only one tool in that mix. It is not even an analytics tool. " That lead to this post.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Occam's Razor

AUGUST 13, 2012

That means: All of these metrics are off. Jeroen Hesterman: My biggest challenge is this: I've created a data platform which captures all campaign (paid) traffic and can attribute a conversion to each based on whatever model I choose. That is the solution. The only challenge is that it is not easy, and everyone wants easy.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. They’ve become “embedded institutions” in engineering. back to the structure of the dataset. intelligibility.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

MARCH 23, 2010

Javascript tag driven click data processed in the cloud provided through a web based front end that allows you to segment and create meaningful views of the data unique to you. There are more mistruths and F U D about Web analytics out there than I think is reasonable. Part of it fueled by Vendors. What a competitive bunch!

Analytics 118

Analytics Consulting Metrics Experimentation 118

Cloudera

JUNE 6, 2019

Over time, workloads start processing more data, tenants start onboarding more workloads, and administrators (admins) start onboarding more tenants. While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ data lake.

Metadata 85

Metadata Data Lake Optimization Strategy 85

Occam's Razor

OCTOBER 19, 2010

An expansive view of what it means to use data online, both from the type of data perspective and the kind of desired impact perspective. They'll simply puke data faster and, if you implement them right, more efficiently. What is the first thing you want when you think about web analytics? Of course tools. spectrum.

Analytics 135

Analytics Testing Measurement Optimization 135

Occam's Razor

MAY 27, 2015

In a recent set of keynotes and consulting engagements in the US, UK and Canada, I've had an overwhelming feeling that in very fundamental ways some companies make imprecise choices when it comes to their digital strategy. Not because they don't have enough money or opportunity or people. Let's go! Can you do RM+RP+RT using TV?

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

datapine

JANUARY 31, 2022

The main use of business intelligence is to help business units, managers, top executives, and other operational workers make better-informed decisions backed up with accurate data. BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions. Let’s see it with a real-world example.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

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. We ought not dredge our data.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

JANUARY 7, 2014

First… it is important to realize that big data's big imperative is driving big action. Reporting Squirrels spend 75% or more of their time in data production activities. Remember none of these jobs will do any data collection/IT work, even in medium-sized companies.) Reporting Squirrels vs. Analysis Ninjas.

Data-driven 140

Data-driven Consulting Reporting Marketing 140

Occam's Razor

APRIL 29, 2013

It turns out that Marketers, especially Digital Marketers, make really silly mistakes when it comes to data. Small data. Many used some data, but they unfortunately used silly data strategies/metrics. Eight data things that marketing people believe that get them fired… 1. You just don't say no!

Marketing 166

Marketing Metrics Advertising Measurement 166