CIO Business Intelligence

NOVEMBER 7, 2022

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects. Are data science teams set up for success? Have business leaders defined realistic success criteria and areas of low-risk experimentation?

Machine Learning 128

Machine Learning Data Science Experimentation IoT 128

Cloudera

JANUARY 30, 2019

It’s official – Cloudera and Hortonworks have merged , and today I’m excited to announce the availability of Cloudera Data Science Workbench (CDSW) for Hortonworks Data Platform (HDP). Trusted by large data science teams across hundreds of enterprises —. Sound familiar? What is CDSW?

Data Science 75

Data Science Machine Learning Experimentation Cost-Benefit 75

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. What Is Model Risk? Types of Model Risk.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

CIO Business Intelligence

APRIL 2, 2024

Joanne Friedman, PhD, CEO, and principal of smart manufacturing at Connektedminds, says orchestrating success in digital transformation requires a symphony of integration across disciplines : “CIOs face the challenge of harmonizing diverse disciplines like design thinking, product management, agile methodologies, and data science experimentation.

Management 140

Management Digital Transformation Testing Experimentation 140

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. Model interpretability continues to spark public discourse among industry. Model interpretability continues to spark public discourse among industry. Insights are drawn from Bin Yu, W.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

Data Science and Beyond

SEPTEMBER 19, 2016

Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. The hardest parts of data science.

Data Science 66

Data Science Predictive Modeling Experimentation Deep Learning 66

CIO Business Intelligence

DECEMBER 19, 2023

Many technology investments are merely transitionary, taking something done today and upgrading it to a better capability without necessarily transforming the business or operating model. If CIOs don’t improve conversions from pilot to production, they may find their investors losing patience in the process and culture of experimentation.

Digital Transformation 143

Digital Transformation Unstructured Data Machine Learning Risk Management 143

Domino Data Lab

MAY 15, 2019

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. Over the years, I have listened to data scientists and machine learning (ML) researchers relay various pain points and challenges that impede their work. These steps also reflect the experimental nature of ML product management.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

The Unofficial Google Data Science Blog

JANUARY 16, 2018

We present data from Google Cloud Platform (GCP) as an example of how we use A/B testing when users are connected. Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. Figure 4 is a visual representation of these steps.

Testing 58

Testing Experimentation Measurement Modeling 58

Domino Data Lab

JUNE 13, 2019

Recently, Chhavi Yadav (NYU) and Leon Bottou (Facebook AI Research and NYU) indicated in their paper, “ Cold Case: The Lost MNIST Digits ”, how they reconstructed the MNIST (Modified National Institute of Standards and Technology) dataset and added 50,000 samples to the test set for a total of 60,000 samples. Did they overfit the test set?

Testing 83

Testing Data Science Experimentation Metadata 83

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. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

CIO Business Intelligence

JULY 6, 2022

Where cloud is most effective: One area I think cloud is just going to be really effective is any area which involves experimentation and has a high opportunity cost. Because when you can experiment, you can potentially enter a new business quickly, test an idea. At its core, it is sophisticated and complex math on data.

Experimentation 98

Experimentation Strategy Testing Risk 98

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

IBM Big Data Hub

NOVEMBER 3, 2023

To add to the complexity, there is a shortage of finding the right people with the right skills to take on development or data science projects. When you make it easier to work with events, other users like analysts and data engineers can start gaining real-time insights and work with datasets when it matters most.

Cost-Benefit 78

Cost-Benefit Experimentation Testing Digital Transformation 78

Cloudera

APRIL 9, 2021

The third video in the series highlighted Reporting and Data Visualization. Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. This integration is key in assuring that models evolve with the data – to avoid, for example, model drift.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

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. The AIgent was built with BERT, Google’s state-of-the-art language model. More relevant to the AIgent is Google’s BERT model, a task-agnostic (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

Domino Data Lab

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. Data Scientists, Tradeoffs, and Data Ethics. As more companies become model-driven , data scientists are uniquely positioned to drive innovation and to help their companies remain economically strong.

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

DataRobot

APRIL 19, 2021

When the machine learning algorithm is trained, data scientists take the outputs (predictions), apply business rules to turn predictions into decisions, and send these predictions and decisions into the organization’s computer systems. They need data assembly, data science, integration, and visualization skills.

Machine Learning 52

Machine Learning Visualization Data-driven Data Science 52

Domino Data Lab

DECEMBER 4, 2020

NCA doesn’t require the assumption of a specific compartmental model for either drug or metabolite; it is instead assumption-free and therefore easily automated [1]. PharmaceUtical Modeling And Simulation (or PUMAS) is a suite of tools to perform quantitative analytics for pharmaceutical drug development [2]. Mean residence time.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Data Science and Beyond

APRIL 5, 2021

The world has adapted quickly, though it seems like Automattic’s globally-distributed model is still quite unusual. Instead, many companies have switched to a locally-remote model, hiring remotely within the same country or timezone region. Only time will tell. Sustainability.

Experimentation 61

Experimentation Testing Publishing Measurement 61

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. 42% of data scientists are solo practitioners or on teams of five or fewer people. 42% of data scientists are solo practitioners or on teams of five or fewer people. Deep Dive into DataRobot Notebooks.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

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

APRIL 7, 2019

A good NLP library will make it easy to both train your own NLP models and integrate with the downstream ML or DL pipeline. A good NLP library should also implement the latest and greatest algorithms and models – not easy while NLP is having its ImageNet moment and state-of-the-art models are being outpaced twice a month.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

FineReport

DECEMBER 11, 2019

In the past few years, the term “data science” has been widely used, and people seem to see it in every field. Big Data”, “Business Intelligence”, “ Data Analysis ” and “ Artificial Intelligence ” came into being. For a while, everyone seems to have begun to learn data analysis.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

O'Reilly on Data

OCTOBER 19, 2021

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 346

IT Testing Experimentation Software 346

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. You can use a regression model. Introduction.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

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

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 307

Testing Machine Learning Consulting Data Quality 307

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

bridgei2i

APRIL 28, 2020

So, there’s a lot of data out there and it’s very hard to figure out what data to use. And so that process with curation or identifying which data potentially is a leading indicator and then test those leading indicators. Are they going to look at, you know, maybe new business models using data?

Enterprise 103

Enterprise Data Analytics Analytics Forecasting 103

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”. Debugging AI Products.

Management 244

Management Metrics Machine Learning Modeling 244

CIO Business Intelligence

MARCH 7, 2023

While many organizations are successful with agile and Scrum, and I believe agile experimentation is the cornerstone of driving digital transformation, there isn’t a one-size-fits-all approach. CIOs should consider technologies that promote their hybrid working models to replace in-person meetings.

Digital Transformation 145

Digital Transformation Data Governance Experimentation Machine Learning 145

Domino Data Lab

JULY 28, 2021

The data science lifecycle (DLSC) has been defined as an iterative process that leads from problem formulation to exploration, algorithmic analysis and data cleaning to obtaining a verifiable solution that can be used for decision making. The data science process in a business environment begins with the Manage stage.

Data Science 59

Data Science Enterprise Modeling Experimentation 59

Rocket-Powered Data Science

OCTOBER 16, 2020

Be sure to listen to the full recording of our lively conversation, which covered Data Literacy, Data Strategy, Data Leadership, and more. The data age has been marked by numerous “hype cycles.” We build models to test our understanding, but these models are not “one and done.” The Age of Hype Cycles.

Digital Transformation 142

Digital Transformation Experimentation Data Strategy Strategy 142

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