Cloudera

APRIL 9, 2021

This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

O'Reilly on Data

MARCH 31, 2020

The model outputs produced by the same code will vary with changes to things like the size of the training data (number of labeled examples), network training parameters, and training run time. This has serious implications for software testing, versioning, deployment, and other core development processes.

Management 332

Management Machine Learning Experimentation Metrics 332

Data Science and Beyond

APRIL 5, 2021

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. My main "day job" focus in 2020 was on being the tech lead for Automattic’s new experimentation platform (ExPlat).

Experimentation 61

Experimentation Testing Publishing Measurement 61

Depict Data Studio

JULY 13, 2021

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! Using scientific methods to test hypotheses (which are what statistics test) are what scientists do; delivering programming and tracking client progress is what nonprofit practitioners do.

Statistics 98

Statistics Testing Recreation/Entertainment Experimentation 98

Dataiku

APRIL 22, 2022

For companies with small datasets and a mandate to move beyond experimentation, Frugal AI promises to be a way to overcome this challenge. Storage infrastructure and data collection/processing costs. Frugal by Design: Why Focus on the Data and Not the Code? Today, in 2022, the code (i.e.,

Experimentation 59

Experimentation Modeling Business Objectives Manufacturing 59

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. So, we have workspaces, projects and sessions in that order.

Machine Learning 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

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. Testing out a new feature.

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? On my test set, this approach resulted in~75–95% accuracy and ~.65

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

Look – ahead bias – This is a common challenge in backtesting, which occurs when future information is inadvertently included in historical data used to test a trading strategy, leading to overly optimistic results. To comply with licensing considerations, we cannot provide a sample of the ETF constituents data.

Snapshot 80

Snapshot Data Lake Testing Strategy 80

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. Transcript.

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? If you have no experience with Web Analytics then you'll learn what it is and the nitty gritty of data collection and core metrics such as Visits and Time on Site and Bounce Rate and Top Destinations etc.

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. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.

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.

Forecasting 59

Forecasting Sales Data-driven Modeling 59

Cloudera

JUNE 6, 2019

We sometimes refer to this as splitting “dev/test” from “production” workloads, but we can generalize the approach by referring to the overall priority of the workload for the business. 3) By workload priority. A third strategy splits clusters based on the overall priority of the workloads running on those clusters.

Metadata 86

Metadata Data Lake Optimization Strategy 86

Occam's Razor

MARCH 23, 2010

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. If you blog that a short on-exit survey or a feedback button is a great way to collect voice of customer, I don't have to be lazy or hyper paranoid and wait for a convincing case study.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Occam's Razor

OCTOBER 19, 2010

If you can show ROI on a DW it would be a good use of your money to go with Omniture Discover, WebTrends Data Mart, Coremetrics Explore. If you have evolved to a stage that you need behavior targeting then get Omniture Test and Target or Sitespect. Move from a data collection obsession and develop a crush on data analysys.

Analytics 135

Analytics Testing Measurement Optimization 135

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. Media-Mix Modeling/Experimentation. Then approach each separately (even though there are tools like Google Analytics that will do both). Tag your mobile website.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

AUGUST 13, 2012

If you want to make the smartest decisions about your budget allocation then leveraging the time tested methodology of media mix modeling (at its core powered by controlled experiments) is the only way to go. The decentralized teams understand the difference between reporting and analysis , and simply focus on fast, hyper-relevant analysis!

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

Remember none of these jobs will do any data collection/IT work, even in medium-sized companies.) But if their primary output is just data, and not actions to take expressed in English or verbally in weekly senior staff meeting, then they are simply Reporting Squirrels. Usability testing (lab based or online).

Data-driven 140

Data-driven Consulting Reporting Marketing 140

Occam's Razor

APRIL 29, 2013

PS: The phrase "real-time data analysis" is an oxymoron. Real-time data is super valuable if zero human beings are involved from data collection to action being taken. Eight Silly Data Things Marketing People Believe That Get Them Fired. PPS: I've mentioned one exception in the past.

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. This is normal.

Marketing 136

Marketing Analytics Machine Learning Strategy 136