Cloudera

APRIL 9, 2021

The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Data Collection – streaming data.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

CIO Business Intelligence

MAY 16, 2023

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data.

Advertising 85

Advertising Recreation/Entertainment Experimentation Enterprise 85

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

Depict Data Studio

JULY 13, 2021

It was also just nice to talk to a group of smart, creative, data-minded professionals who also (yes – that’s right – also) feel overwhelmed by the prospect of having to use statistics. In the science world, if you have a small group of people and do not find statistical significance, one thing you can do is test a much bigger group!

Statistics 98

Statistics Testing Recreation/Entertainment Experimentation 98

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

Dataiku

APRIL 22, 2022

Enter Frugal AI , a technique that promises the use of less data and less compute power while guaranteeing robustness within the intended field of use for a given AI model.It In this blog series, we will explore Frugal AI from the perspectives of data, design, trust, and sustainability. It's All in a Name.

Experimentation 59

Experimentation Modeling Business Objectives Manufacturing 59

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. But it is not routine.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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

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

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

AWS Big Data

JULY 3, 2023

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. Amazon Simple Storage Service (Amazon S3) is a popular cloud-based object storage service that can be used as the foundation for building a data lake.

Snapshot 82

Snapshot Data Lake Testing Strategy 82

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

Occam's Razor

JUNE 6, 2007

Experimentation & Testing (A/B, Multivariate, you name it). What ideas to test first on your site? I am thrilled to say that my book Web Analytics: An Hour A Day has been published and is now widely available. Thrilled is perhaps understating it, I am giddy like a schoolgirl. There I said it. Qualitative and quantitative.

Analytics 60

Analytics Metrics Marketing Dashboards 60

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

DataRobot Blog

OCTOBER 4, 2022

However, hand-coding, testing, evaluating and deploying highly accurate models is a tedious and time-consuming process. Manually scaling out this process to thousands of stores or SKUs at once and then monitoring them, for example, is a nightmarish experience for data scientists. Improved Productivity. The Dataset.

Forecasting 59

Forecasting Sales Data-driven Modeling 59

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

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

Occam's Razor

SEPTEMBER 15, 2014

In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation. Mobile devices (phones, tablets, wearables) are transforming how we behave, how we buy, how we consume content, and dare I say how we become happy or we become sad. Many reasons.

Metrics 141

Metrics Reporting Analytics Marketing 141

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. What is the first thing you want when you think about web analytics? Of course tools. What to do, where to start, what's cool. recommending tools for the complete web analytics 2.0 spectrum.

Analytics 135

Analytics Testing Measurement Optimization 135

Occam's Razor

AUGUST 13, 2012

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. What you are asking for is impossible.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Occam's Razor

JULY 18, 2011

Most email programs now have preview panes that typically block images and scripts (Outlook, Thunderbird, Gmail, everyone), and default settings prevent data collection due to concerns about viruses. This should drive aggressive experimentation of email content / offers / targeting / every facet by your team. That is okay.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

datapine

JANUARY 31, 2022

The companies that are most successful at marketing in both B2C and B2B are using data and online BI tools to craft hyper-specific campaigns that reach out to targeted prospects with a curated message. Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. We must correct for multiple hypothesis tests. 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. Real-time data is life changing. It seems absolutely stupid to say, " No, I don't want real-time data. "

Marketing 166

Marketing Metrics Advertising Measurement 166

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. When you go to the interview, the hiring company will proceed to ask questions that test your competency in the listed job requirements. It is now all on you. This is normal.

Marketing 136

Marketing Analytics Machine Learning Strategy 136