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 314

Management Machine Learning Metrics Modeling 314

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

DataKitchen

SEPTEMBER 21, 2023

The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

O'Reilly on Data

DECEMBER 9, 2019

Because of this trifecta of errors, we need dynamic models that quantify the uncertainty inherent in our financial estimates and predictions. Practitioners in all social sciences, especially financial economics, use confidence intervals to quantify the uncertainty in their estimates and predictions.

Statistics 32

Statistics Uncertainty Risk Marketing 32

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do. Testing out a new feature. Form a hypothesis.

Metrics 157

Metrics KPI Analytics Key Performance Indicator 157

Cloudera

DECEMBER 3, 2021

This involves identifying, quantifying and being able to measure ethical considerations while balancing these with performance objectives. Systems should be designed with bias, causality and uncertainty in mind. Uncertainty is a measure of our confidence in the predictions made by a system. System Design. Model Drift.

Uncertainty 102

Uncertainty Measurement Metrics Risk 102

CIO Business Intelligence

JUNE 8, 2022

Digital disruption, global pandemic, geopolitical crises, economic uncertainty — volatility has thrown into question time-honored beliefs about how best to lead IT. Thriving amid uncertainty means staying flexible, he argues. . The coming months are a leadership test for CIOs, and it’s a pass/fail grade.”. Keep calm and lead on.

IT 133

IT Cost-Benefit Uncertainty Digital Transformation 133

The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Researchers and practitioners have been using human-labeled data for many years, trying to understand all sorts of abstract concepts that we could not measure otherwise. That’s the focus of this blog post.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

CIO Business Intelligence

AUGUST 1, 2024

The measures take effect in stages: Affected companies have to follow the first rules in just six months. The implementation must not become a stalemate for companies: Long legal uncertainty , unclear responsibilities and complex bureaucratic processes in the implementation of the AI Act would hinder European AI innovation.

Risk 119

Risk Uncertainty Testing Digital Transformation 119

CIO Business Intelligence

FEBRUARY 26, 2024

He points to a recent observation from GitHub CEO Thomas Dohmke, who noted 40% of computer-generated code was adopted by developers beta testing its Copilot AI automated code-writing system. The test also cut programming time by 55%. “Many people believe this will increase to 80%,” Mehlkopf said. “If

IT 128

IT Uncertainty Strategy Testing 128

AWS Big Data

SEPTEMBER 5, 2024

Intuitively, for some extremely short user inputs, the vectors generated by dense vector models might have significant semantic uncertainty, where overlaying with a sparse vector model could be beneficial. load(split="test") ingest_dataset(corpus, aos_client=aos_client, index_name=index_name) 3. How to combine dense and sparse?

Metrics 111

Metrics Testing Experimentation Modeling 111

Smart Data Collective

APRIL 30, 2020

We are also required to follow the same restrictive measures that attempt to contain or mitigate the spread of the virus. Advisable cybersecurity measures. Security measures like VPNs and multi-factor authentication (MFA) may be necessary to secure a home office. Here are some of the ways that these can be achieved.

Measurement 93

Measurement Uncertainty Risk Testing 93

Jedox

SEPTEMBER 3, 2020

The unprecedented uncertainty forced companies to make critical decisions within compressed time frames. Using these drivers as an overlay to stress-test models add robustness to forecasting and can identify exposure and risks to long-term stability. This placed an acute spotlight on planning agility. Conclusion.

Uncertainty 81

Uncertainty Forecasting Digital Transformation Risk 81

DataRobot

JULY 16, 2021

Accuracy — this refers to a subset of model performance indicators that measure a model’s aggregated errors in different ways. Testing your model to assess its reproducibility, stability, and robustness forms an essential part of its overall evaluation. Recognizing and admitting uncertainty is a major step in establishing trust.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. A single model may also not shed light on the uncertainty range we actually face. For example, we may prefer one model to generate a range, but use a second scenario-based model to “stress test” the range.

Forecasting 115

Forecasting Data Science Statistics Uncertainty 115

Data Science and Beyond

JANUARY 7, 2019

Therefore, bootstrapping has been promoted as an easy way of modelling uncertainty to hackers who don’t have much statistical knowledge. Confidence intervals are a common way of quantifying the uncertainty in an estimate of a population parameter. Don’t compare confidence intervals visually.

Statistics 77

Statistics Uncertainty Testing Modeling 77

CIO Business Intelligence

APRIL 28, 2024

However, new energy is restricted by weather and climate, which means extreme weather conditions and unpredictable external environments bring an element of uncertainty to new energy sources. It was the solution of choice to achieve an observable, measurable, adjustable, controllable and traceable low-voltage side. HPLC can deliver 99.9%

Interactive 95

Interactive Uncertainty Data Collection IoT 95

DataRobot

APRIL 5, 2021

The uncertainty in her reply piqued my interest. In a series of experiments, the researchers and authors of “ Manipulating and Measuring Model Interpretability ” asked participants to predict apartment prices with the assistance of a machine learning model. Umm, yes, I think so,” she replied. I wanted to know why she was so uncertain.

Machine Learning 96

Machine Learning Uncertainty Modeling Reporting 96

Cloudera

DECEMBER 11, 2020

Insurance and finance are two industries that rely on measuring risk with historical data models. In “Are Your Machine Learning Models Wrong” , Richard Harmon explores what financial institutions should do in the face of the uncertainty caused by COVID-19. Data Variety.

Insurance 95

Insurance Digital Transformation Unstructured Data Machine Learning 95

datapine

SEPTEMBER 4, 2023

Your Chance: Want to test a powerful data visualization software? Your Chance: Want to test a powerful data visualization software? It measured the positions, motions, and distances of over 100.000 stars, and it had a major impact on much of the astronomy research that has been carried out to this date.

Interactive 264

Interactive Visualization Cost-Benefit Dashboards 264

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 96

Experimentation Statistics Testing Knowledge Discovery 96

Cloudera

OCTOBER 26, 2021

The challenges of remote working with dispersed teams have been a test of leadership. That being said, leaders should take a measured approach and refrain from jumping right in every single time the team encounters an issue. Are you someone who leads from an ivory tower or from the frontlines? Emphasise commitment in times of change.

Uncertainty 87

Uncertainty Interactive Reporting Measurement 87

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Crucially, it takes into account the uncertainty inherent in our experiments. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

CIO Business Intelligence

OCTOBER 18, 2023

If anything, 2023 has proved to be a year of reckoning for businesses, and IT leaders in particular, as they attempt to come to grips with the disruptive potential of this technology — just as debates over the best path forward for AI have accelerated and regulatory uncertainty has cast a longer shadow over its outlook in the wake of these events.

Risk 141

Risk Manufacturing Enterprise Technology 141

CIO Business Intelligence

JULY 7, 2023

This module validates your ability to measure, assess, and develop the Service Desk practice capability using the ITIL Maturity Model. You’ll be tested on a situation of your choosing, so the material will be personal to your experience.

Cost-Benefit 98

Cost-Benefit Strategy Management Uncertainty 98

Sisense

OCTOBER 6, 2020

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. This type of data is often collected through less rigid, measurable means than quantitative data. This is quantitative data.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

CIO Business Intelligence

JANUARY 15, 2024

Then she advises practice: Work out stories first with peers or mentors to test whether the stories inspire the desired responses or convey the intended messages. Anytime you’re starting down a pathway of change, you have to talk to people you trust, let them know what you’re working on, and then set a measuring stick,” Pyle says.

IT 131

IT Consulting Strategy Sales 131

Domino Data Lab

JUNE 23, 2019

As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. These measurement-obsessed companies have an advantage when it comes to AI. Testing is critical. It is similar to R&D. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

JULY 12, 2010

Let's go look at some tools… Measuring "Invisible Virality": Tynt. It measures how often a blog post is tweeted/retweeted. I also measure the # of Comments Per Post as a measure of how "engaging" / "valuable" people found the content to be. Or for that matter how many tools.

Analytics 97

Analytics Measurement Metrics KPI 97

BI-Survey

FEBRUARY 14, 2022

Overnight, the impact of uncertainty, dynamics and complexity on markets could no longer be ignored. Local events in an increasingly interconnected economy and uncertainties such as the climate crisis will continue to create high volatility and even chaos. The COVID-19 pandemic caught most companies unprepared. BARC Recommendations.

Forecasting 52

Forecasting Uncertainty Measurement Cost-Benefit 52

CIO Business Intelligence

JULY 10, 2024

On top of this, Relex added instructions to its prompt to avoid answering any questions outside the company’s knowledge base, he says, and to express uncertainty when the question was at the limits of its knowledge or skills. Other hyperscalers also offer guardrails that work with their gen AI platforms.

Enterprise 136

Enterprise Risk Modeling Risk Management 136

Insight

FEBRUARY 19, 2020

I held out 20% of this as a test set and used the remainder for training and validation. The genre uniqueness is a measure of how unique a movie’s combination of genre categories is relative to all movies in my data set. Below is the result of a single XGBoost model trained on 80% of the data and tested on the unseen held-out 20%.

Risk 67

Risk ROI Modeling Metrics 67

The Unofficial Google Data Science Blog

APRIL 19, 2021

To explain, let’s borrow a quote from Nate Silver’s The Signal and the Noise : One of the most important tests of a forecast — I would argue that it is the single most important one — is called calibration. The numerical value of the signal became decoupled from the event it was measuring even as the ordinal value remained unchanged.

Modeling 122

Modeling IT Metrics Testing 122

Data Science and Beyond

JULY 28, 2017

Hence, Automattic relies heavily on textual channels, and text-based interviews allow the company to test the written communication skills of candidates. The answers were that I’d be joining the data science team, and that the next steps are a pre-trial test, a paid trial, and a final interview with Matt. And after 2.5

Data Science 81

Data Science Testing Measurement Uncertainty 81

DataRobot

JUNE 1, 2021

You should first identify potential compliance risks, with each additional step again tested against risks. Recognizing and admitting uncertainty is a major step in establishing trust. Interventions to manage uncertainty in predictions vary widely. Knowing When to Trust a Model. Is rain 40% likely?

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 42

Experimentation Statistics Metrics Measurement 42

The Unofficial Google Data Science Blog

APRIL 16, 2019

Such decisions involve an actual hypothesis test on specific metrics (e.g. The metrics to measure the impact of the change might not yet be established. Typically, it takes a period of back-and-forth between logging and analysis to gain the confidence that a metric is actually measuring what we designed for it to measure.

Experimentation 69

Experimentation Sales Metrics Measurement 69