AWS Big Data

NOVEMBER 2, 2023

AWS benchmark The AWS team performed benchmark tests on Spark workloads with Graviton2 on EMR Serverless using the TPC-DS 3 TB scale performance benchmarks. 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.

Cost-Benefit 91

Cost-Benefit Big Data Testing Optimization 91

Domino Data Lab

AUGUST 6, 2021

An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. The complexity of models, and general limitations of expertise with data science among business leaders, creates an environment ripe for risk. Types of Model Risk.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. A/B testing is used widely in information technology companies to guide product development and improvements.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Domino Data Lab

NOVEMBER 14, 2019

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. “Instead of general interpretability, we focus on the use of interpretations to produce insight from ML models as part of the larger data–science life cycle. ” and advocate.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”. And an LSOS is awash in data, right?

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Domino Data Lab

DECEMBER 4, 2020

This tutorial will show how easy it is to integrate and use Pumas in the Domino Data Science Platform , and we will carry out a simple non-compartmental analysis using a freely available dataset. The Domino data science platform empowers data scientists to develop and deliver models with open access to the tools they love.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Sisense

JANUARY 21, 2020

In general, it is not possible to give a rule of thumb about when data should be partitioned or combined. A new drug promising to reduce the risk of heart attack was tested with two groups. They measured both the blood pressure of the participants and if they had a heart attack or not. It really depends on the circumstances.

Testing 104

Testing Risk Data-driven Statistics 104

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 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

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. 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

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. The AGI analyzes the data and identifies a rare genetic mutation linked to a specific disease.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

Insight

APRIL 28, 2020

Originally posted on Open Data Science (ODSC). In this article, we share some data-driven advice on how to get started on the right foot with an effective and appropriate screening process. Designing a Data Science Interview Onsite interviews are indispensable, but they are time-consuming.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

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. I tested several different flavors of BERT for use as synopsis classifiers before settling on the DistilBERT model from Hugging Face.

Modeling 59

Modeling Metadata Publishing Sales 59

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. The case that might be familiar to you is an AB test. A complementary Domino project is available. .

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Alation

SEPTEMBER 28, 2022

DataOps strategies share these common elements: Collaboration among data professionals and business stakeholders. Easy-to-experiment data development environment. Automated testing to ensure data quality. There are many inefficiencies that riddle a data pipeline and DataOps aims to deal with that. Embrace change.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

O'Reilly on Data

OCTOBER 19, 2021

It has far-reaching implications as to how such applications should be developed and by whom: ML applications are directly exposed to the constantly changing real world through data, whereas traditional software operates in a simplified, static, abstract world which is directly constructed by the developer. Data Science Layers.

IT 346

IT Testing Experimentation Software 346

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

The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 95

Experimentation Statistics Testing Strategy 95

DataKitchen

APRIL 13, 2021

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Genie — Distributed big data orchestration service by Netflix.

Testing 300

Testing Machine Learning Consulting Data Quality 300

bridgei2i

APRIL 28, 2020

And obviously, such times call for truly unprecedented measures. Since you’re one of the top global influencers in the world of data and analytics, we wanted to take a little bit of time today to talk to you and understand how data and analytics can really help in a crisis.To Doug Laney: My pleasure. Aruna: Got it.

Enterprise 103

Enterprise Data Analytics Analytics Forecasting 103

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

O'Reilly on Data

OCTOBER 13, 2020

In Bringing an AI Product to Market , we distinguished the debugging phase of product development from pre-deployment evaluation and testing. During testing and evaluation, application performance is important, but not critical to success. require not only disclosure, but also monitored testing. Debugging AI Products.

Management 244

Management Metrics Machine Learning Modeling 244

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

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

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. It is similar to R&D.

Management 110

Management Machine Learning Experimentation Metrics 110

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. At the end of the semester, you administer a proficiency test to the students and compare test performance across the groups. When analyzing the outcome measure (e.g.,

Experimentation 42

Experimentation Testing Measurement Metrics 42

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

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

MAY 31, 2016

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. 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. 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

This control prevents overfitting and reduction in predictive accuracy on new test data. Models are at the heart of of data science and increasing predictive accuracy of models is a core expectation upon industry. We can take this model to other, novel data and perform a final hold-out test assessment.

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. 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. Thankfully not only have modern data analysis tools made data collection cheap and easy, they have made the process of exploratory data analysis cheaper and easier as well. We must correct for multiple hypothesis tests.

Experimentation 61

Experimentation Testing Statistics Metrics 61