AWS Big Data

NOVEMBER 2, 2023

It showcases the potential of Graviton2 in delivering enhanced price-performance ratios, making it an attractive choice for organizations seeking to optimize their big data workloads. GoDaddy benchmark During our initial experimentation, we observed that arm64 on EMR Serverless consistently outperformed or performed on par with x86_64.

Cost-Benefit 84

Cost-Benefit Big Data Testing Optimization 84

The Unofficial Google Data Science Blog

JANUARY 16, 2018

Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. With A/B testing, we can validate various hypotheses and measure the impact of our product changes, allowing us to make better decisions. This could create confusion.

Testing 58

Testing Experimentation Measurement Modeling 58

Cloudera

FEBRUARY 16, 2022

blueberry spacing) is a measure of the model’s interpretability. In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. The complete list is shown below: Model Lineage .

Machine Learning 86

Machine Learning Modeling Metadata Recreation/Entertainment 86

Sisense

JANUARY 21, 2020

One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. They measured both the blood pressure of the participants and if they had a heart attack or not. To better understand when the data should be grouped, you should be familiar with causal inference.

Testing 104

Testing Risk Data-driven Statistics 104

Insight

MARCH 12, 2020

With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. of users tagged Cuckoo’s Calling with ‘science-fiction’, but 10% of users tagged it with ‘mystery’, we should be more confident in the latter label.

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

O'Reilly on Data

OCTOBER 13, 2020

Ideally, AI PMs would steer development teams to incorporate I/O validation into the initial build of the production system, along with the instrumentation needed to monitor model accuracy and other technical performance metrics. But in practice, it is common for model I/O validation steps to be added later, when scaling an AI product.

Management 244

Management Metrics Machine Learning Modeling 244

DataKitchen

APRIL 13, 2021

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

Testing 307

Testing Machine Learning Consulting Data Quality 307

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. This blog post introduces the notions of representational uncertainty and interventional uncertainty to paint a fuller picture of what the practicing data scientist is up against. Vignette: Data Science at fluff.ai

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

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. These measurement-obsessed companies have an advantage when it comes to AI. That’s another pattern.

Management 110

Management Machine Learning Experimentation Metrics 110

The Unofficial Google Data Science Blog

JUNE 30, 2016

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

Statistics 44

Statistics Optimization Modeling Experimentation 44

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. Such decisions involve an actual hypothesis test on specific metrics (e.g.

Experimentation 68

Experimentation Sales Metrics Measurement 68

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. back to the structure of the dataset. Let’s look through some antidotes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

JULY 31, 2016

The analysis can be straightforward, especially when it's safe to assume that individual observations of an outcome measure are independent. The outcome measure we care about is an average of the students' test scores, and so the unit of observation is a student. To figure this out, let's consider an appropriate experimental design.

Experimentation 42

Experimentation Testing Measurement Metrics 42

The Unofficial Google Data Science Blog

MARCH 22, 2018

But what if users don't immediately uptake the new experimental version? Background At Google, experimentation is an invaluable tool for making decisions and inference about new products and features. Naturally, this issue is of particular concern to us in the Play Data Science team.

Experimentation 62

Experimentation Measurement Modeling Statistics 62

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant. The result is that experimenters can’t afford to be sloppy about quantifying uncertainty. In statistics, such segments are often called “blocks” or “strata”.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

The Unofficial Google Data Science Blog

MAY 31, 2016

It is important that we can measure the effect of these offline conversions as well. Panel studies make it possible to measure user behavior along with the exposure to ads and other online elements. Wouldn't it be great if we didn't require individual data to estimate an aggregate effect? days or weeks).

Advertising 42

Advertising Testing Sales Statistics 42

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

Our post describes how we arrived at recent changes to design principles for the Google search page, and thus highlights aspects of a data scientist’s role which involve practicing the scientific method. There has been debate as to whether the term “data science” is necessary. Some don’t see the point.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Domino Data Lab

AUGUST 26, 2019

Models are at the heart of of data science and increasing predictive accuracy of models is a core expectation upon industry. The project also covers building a pipeline for automating ML workflow and stay tuned for additional hyperparamater content on the Domino Data Science blog. In [1]: # setup. Best Score: 0.98

Testing 79

Testing Modeling Machine Learning Metrics 79

datapine

JANUARY 31, 2022

Because things are changing and becoming more competitive in every sector of business, the benefits of business intelligence and proper use of data analytics are key to outperforming the competition. Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data. The results?

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

DataRobot

APRIL 29, 2021

This list includes: Rachik Laouar is Head of Data Science for the Adecco Group. Rachik is working to transform that company’s products through data analytics and AI and will be speaking on the topic, Executive Track: Turning an Industry Upside Down. . Eric Weber is Head of Experimentation And Metrics for Yelp.

Machine Learning 59

Machine Learning Experimentation Data-driven Data Science 59

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1: The Himalaya mountain range Much of the data available to us in big data settings is large not only in the number of observations, but also in the number of features. With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis.

Experimentation 61

Experimentation Testing Statistics Metrics 61