AWS Big Data

JUNE 6, 2023

Founded in 2012, SumUp is the financial partner for more than 4 million small merchants in over 35 markets worldwide, helping them start, run and grow their business. The Data Science teams also use this data for churn prediction and CLTV modeling. This is a guest blog post by Mira Daniels and Sean Whitfield from SumUp.

Analytics 55

Analytics Data Lake Testing Optimization 55

AWS Big Data

SEPTEMBER 18, 2023

Recently, AWS announced the general availability of Amazon CodeWhisperer , an AI coding companion that uses foundational models under the hood to improve software developer productivity. Many customers are interested in boosting productivity in their software development lifecycle by using generative AI.

Data Integration 92

Data Integration Big Data Interactive Software 92

Howard Dresner

AUGUST 27, 2013

Before the perfect storm, our tweetchat tribe (comprised of customers, vendors and consultants/analysts) were of the opinion that the growing “app” mentality for “cool stuff” among consumers and the easy-to-consume info in mobile apps could end up increasing trust and thus lead to less testing and faster releases.

Business Intelligence 60

Business Intelligence Finance Risk Data Processing 60

O'Reilly on Data

SEPTEMBER 19, 2023

Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.” And it was good.

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

Insight

JANUARY 14, 2020

CNNs have been widely considered state-of-the-art tools for computer vision since 2012, when AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). a “pre-trained” model) to be recycled and reused for many different tasks. You can use this metric to track progress during training or choose between different models.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

APRIL 3, 2019

Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. As the story goes, the general history of DG is punctuated by four eras: “Application Era” (1960–1990) – some data modeling, ?though There are models everywhere.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

The Unofficial Google Data Science Blog

JUNE 30, 2016

A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means. The choice of space $cal F$ (sometimes called the model ) and loss function $L$ explicitly defines the estimation problem. the curse of dimensionality).

Statistics 44

Statistics Optimization Modeling Experimentation 44

Insight

APRIL 16, 2019

While this model fuels many of today’s businesses on the internet, it comes with a significant tradeoff: an unprecedented amount of user data has been stock piled and is at risk of being exposed through security breaches.

Machine Learning 68

Machine Learning Software Risk Reporting 68

AWS Big Data

JANUARY 30, 2023

Therefore, all this operational data needs to be sent to Delta Lake in near-real time so that the organization can perform various analytics, and build machine learning (ML) models to serve their customers in a more efficient and cost-effective way. On the IAM console, choose Polices in the navigation pane. Choose Create policy.

Insurance 75

Insurance Data Lake Data-driven Management 75

Sisense

MAY 26, 2020

While training a model for NLP, words not present in the training data commonly appear in the test data. Because of this, predictions made using test data may not be correct. Using the semantic meaning of words it already knows as a base, the model can understand the meanings of words it doesn’t know that appear in test data.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

Occam's Razor

OCTOBER 15, 2012

The data was collected in the first part of 2012, between January and May for the Barometer and between January and February for the Enumeration. Think of it as attribution modeling. :). What you see is for 2012. The Analysis: Four Insightful Options. You can also plot a lot of data by continents. We don't.

Advertising 136

Advertising Marketing Strategy Insurance 136

CIO Business Intelligence

APRIL 15, 2022

MIT Technology Review has chronicled a number of failures, most of which stem from errors in the way the tools were trained or tested. For example, Driggs’ group found that their own model was flawed because it was trained on a data set that included scans of patients that were lying down while scanned and patients that were standing up.

Analytics 145

Analytics Machine Learning Insurance Testing 145

The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Cloudera

NOVEMBER 17, 2021

There’s recognition that it’s nearly impossible to find the unicorn data scientist that was the apple of every CEO’s eye in 2012. In addition to the UI interface, Cloudera Machine Learning exposes a REST API that can be used to programmatically perform operations related to Projects, Jobs, Models, and Applications.

Machine Learning 121

Machine Learning Visualization Data Science Metrics 121

Domino Data Lab

APRIL 21, 2021

We’ll use a gradient boosting technique via XGBoost to create a model and I’ll walk you through steps you can take to avoid overfitting and build a model that is fit for purpose and ready for production. This is to prevent any information leakage into our test set. 2f%% of the test set."

Statistics 69

Statistics Machine Learning Modeling Metrics 69

CIO Business Intelligence

MARCH 22, 2022

A certified lead auditor, hacking forensic investigator, and security analyst, Tiwari is an IT veteran from the Indian Navy where he set up the information security and incident response organization, including the formulation of policy frameworks, security audits, product testing, cyber forensics, and risk management. .

Internet Publishing and Broadcasting 98

Internet Publishing and Broadcasting Digital Transformation Insurance Recreation/Entertainment 98

Depict Data Studio

NOVEMBER 4, 2022

I published my first video on November 4, 2012…. Can I hire you to help me prep for the Excel tests that I’ll have to take as part of the hiring process?” Being a full-time YouTuber is an entirely different business model from being a dataviz speaker and course creator. I didn’t create the test!! Most Views.

Dashboards 75

Dashboards Testing Software Consulting 75

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40

DataRobot Blog

APRIL 4, 2022

MMM stands for Marketing Mix Model and it is one of the oldest and most well-established techniques to measure the sales impact of marketing activity statistically. As with any type of statistical model, data is key and GIGO (“Garbage In, Garbage Out”) principle definitely applies. Classical Modeling Considerations. What is MMM?

Machine Learning 52

Machine Learning Sales Measurement ROI 52

Domino Data Lab

AUGUST 8, 2019

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. then building machine learning models to recommend methods and potential collaborators to scientists. The real fun begins once models get deployed in production.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

IBM Big Data Hub

MARCH 29, 2024

GraphQL GraphQL is a query language and API runtime that Facebook developed internally in 2012 before it became open source in 2015. Tools like GraphiQL and GraphQL Playground provide powerful, in-browser, integrated development environments (IDEs) for exploring and testing GraphQL APIs.

Interactive 80

Interactive Digital Transformation Management Optimization 80

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. Structure of a geo experiment A typical geo experiment consists of two distinct time periods: pretest and test. After the test period finishes, the campaigns in the treatment group are reset to their original configurations.

Advertising 42

Advertising Testing Sales Statistics 42

O'Reilly on Data

JULY 25, 2023

I’m a professor who is interested in how we can use LLMs (Large Language Models) to teach programming. Swift Papers felt like a well-scoped project to test how well AI handles a realistic yet manageable real-world programming task. Setting the Stage: Who Am I and What Am I Trying to Build?

Consulting 246

Consulting Interactive Software IT 246

AWS Big Data

OCTOBER 23, 2023

Before you run these workloads, most customers run SQL queries to interactively extract, filter, join, and aggregate data into a shape that can be used for decision-making, model training, or inference. This is a simplified model where we don’t need to use AWS Lake Formation data sharing.

Data Lake 98

Data Lake Visualization Optimization Interactive 98

BizAcuity

MAY 10, 2022

Amazon strategically went with the pricing model of ‘on-demand’, allowing developers to pay only as-per their computational needs. 2012: Amazon Redshift, the first of its kind cloud-based data warehouse service comes into existence. AWS rolls out SageMaker, designed to build, train, test and deploy machine learning (ML) models.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

Insight

DECEMBER 18, 2019

In my last post , we went back to the year 1943, tracking neural network research from the McCulloch & Pitts paper , “ A Logical Calculus of Ideas Immanent in Nervous Activity ” to 2012, when “ AlexNet ” became the first CNN architecture to win the ILSVRC. Let’s get started! the cost function).

Optimization 85

Optimization Machine Learning Testing Visualization 85

Domino Data Lab

MARCH 3, 2019

What’s been the impact of using ML models on culture and organization? Who builds their models? We also used maturity , in other words how long had an enterprise organization been deploying ML models in production? “Three surveys of AI adoption reveal key advice from more mature practices”. Or something. Technologies.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Occam's Razor

JANUARY 10, 2011

10% of your time should be spent in implementing tools, not 15 months with an eye towards analysis in the middle of 2012. " This was in context of a President Obama A/B test. A/B testing! You can win with Omniture or WebTrends or IBM or Google. Stop switching tools! After five years of success worry about tools.

Analytics 120

Analytics Measurement Data-driven Optimization 120

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238

Cloudera

DECEMBER 12, 2018

I decided to jump ship in May of 2012 joining Cloudera. And it [MPP] is a much more efficient execution model. With a traditional relational database, you have to do all the upfront data modeling and have very clean data before it even gets into the tables. At that point, there was no publicly known project called Impala.

Data Warehouse 41

Data Warehouse Marketing Big Data Data Lake 41

Domino Data Lab

SEPTEMBER 25, 2019

One of the most common ways of fitting time series models is to use either autoregressive (AR), moving average (MA) or both (ARMA). These models are well represented in R and are fairly easy to work with. AR models can be thought of as linear regressions of the current value of the time series against previous values.

Forecasting 87

Forecasting Modeling Statistics Optimization 87

CIO Business Intelligence

FEBRUARY 3, 2023

IBM Cloud Pak for Business Automation , for example, provides a low-code studio for testing and developing automation strategies. AI Builder can also create and deploy models that make predictions and even decisions, taking more work off of users’ shoulders. AI tools provide optical character recognition for documents.

Data-driven 145

Data-driven Interactive Enterprise Statistics 145

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Yet when we use these tools to explore data and look for anomalies or interesting features, we are implicitly formulating and testing hypotheses after we have observed the outcomes. We must correct for multiple hypothesis tests. In addition to the issues above, does the conclusion pass the smell test?

Experimentation 61

Experimentation Testing Statistics Metrics 61

datapine

DECEMBER 28, 2021

To make sure the reliability is high, there are various techniques to perform – the first of them being the control tests, which should have similar results when reproducing an experiment in similar conditions. In 2012, the global mean temperature was measured at 58.2 We took a very obvious one to show you. Source: Bill Grueskin.

Statistics 363

Statistics Advertising Visualization Data mining 363

datapine

MAY 17, 2021

Businesses can benefit from improved data driven decision making as well as enhanced business processes and models and share insights across departments more fluently while propelling intelligent business strategies. What is a discovery model, and how do you use it in a real-world business context? What is a data discovery platform?

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161

Jet Global

SEPTEMBER 22, 2023

You can create a query like this: “Please analyze this dataset and let me know interesting facts you see: Rows: (All) Quarter 1, 2012 Quarter 2, 2012 Quarter 3, 2012 … Cells: 4,117,344.28 You might have a dataset represented in a chart, and you want ChatGPT to analyze it.

Dashboards 52

Dashboards Data-driven Visualization Interactive 52