DataRobot

AUGUST 5, 2021

AI and Uncertainty. Some people react to the uncertainty with fear and suspicion. Recently published research addressed the question of “ When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making.”. People are unsure about AI because it’s new. AI you can trust.

Uncertainty 98

Uncertainty Sales Risk Statistics 98

CIO Business Intelligence

MARCH 18, 2024

The researchers discovered that workers who used three of these technologies — AI and machine learning software, wearable trackers for surveillance, and robotics — reported a lower level of health-related well-being, according to a widely used standardized measurement. using wearables, 20.8% AI software, and 23.7%

Technology 81

Technology Uncertainty Machine Learning Interactive 81

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

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 100

Uncertainty Measurement Metrics Risk 100

Smart Data Collective

JULY 1, 2023

Image annotation is the act of labeling images for AI and machine learning models. The resulting structured data is then used to train a machine learning algorithm. There are a lot of image annotation techniques that can make the process more efficient with deep learning.

Metrics 64

Metrics Machine Learning Uncertainty Deep Learning 64

O'Reilly on Data

SEPTEMBER 15, 2020

Technical sophistication: Sophistication measures a team’s ability to use advanced tools and techniques (e.g., PyTorch, TensorFlow, reinforcement learning, self-supervised learning). Technical competence: Competence measures a team’s ability to successfully deliver on initiatives and projects. Conclusion.

Cost-Benefit 290

Cost-Benefit Advertising ROI Machine Learning 290

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. After navigating the complexity of multiple systems and stages to bring data to its end-use case, the final product’s value becomes the ultimate yardstick for measuring success.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

DataRobot

JULY 16, 2021

The first is trust in the performance of your AI/machine learning model. In performance, the trust dimensions are the following: Data quality — the performance of any machine learning model is intimately tied to the data it was trained on and validated against. Dimensions of Trust.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

Jen Stirrup

NOVEMBER 15, 2022

The foundation should be well structured and have essential data quality measures, monitoring and good data engineering practices. Of course, the findings need to add value, but how do we measure this success? Measures can be financial, tying in with the business strategy. After all, it can sound a bit woolly!

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

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.”. While useful, these constructs are not beyond criticism.

Management 240

Management Metrics Machine Learning Modeling 240

Smart Data Collective

APRIL 18, 2019

Everyone wants to leverage machine learning, behavior analytics, and AI so IT teams can “up the ante” against attackers. The reality is that “AI solutions” today are based more in machine learning and behavior analytics , which does NOT equate to higher levels of human intelligence and complex decision making.

Behavioral Analytics 77

Behavioral Analytics Advertising Uncertainty Machine Learning 77

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. . When it comes to data and analytics, test, learn and recalibrate. Some hires may need to be postponed.

IT 132

IT Cost-Benefit Uncertainty Digital Transformation 132

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 Software 52

DataRobot

JUNE 1, 2021

AI and machine learning are fields rife with potential security issues. Recognizing and admitting uncertainty is a major step in establishing trust. Interventions to manage uncertainty in predictions vary widely. Protecting Sensitive Data. Knowing When to Trust a Model. Is rain 40% likely? Deploying Good Governance.

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

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. What if 2020 is an anomaly?

Insurance 94

Insurance Digital Transformation Unstructured Data Machine Learning 94

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.

Uncertainty 96

Uncertainty Machine Learning Modeling Reporting 96

AWS Big Data

AUGUST 7, 2023

Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future. Amazon Redshift ML makes it easy for data analysts and database developers to create, train, and apply machine learning (ML) models using familiar SQL commands in Amazon Redshift. UCI Machine Learning Repository.

Forecasting 86

Forecasting Data Warehouse Machine Learning Modeling 86

CIO Business Intelligence

FEBRUARY 23, 2023

The former rely on batch processing to train machine learning models and generate features (measurable properties of a phenomenon). He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing. Artificial Intelligence, IT Leadership

Uncertainty 75

Uncertainty Strategy Data-driven Machine Learning 75

CIO Business Intelligence

JULY 5, 2022

Even with countless ambiguities present in legal and compliance arenas and ongoing uncertainty in the global, national and regional data privacy landscape, the relevance of a sovereign cloud as part of the journey toward digital sovereignty is more significant now than ever before.

Uncertainty 80

Uncertainty Metadata Machine Learning Data Governance 80

CIO Business Intelligence

JUNE 30, 2022

Even with countless ambiguities present in legal and compliance arenas and ongoing uncertainty in the global, national and regional data privacy landscape, the relevance of a sovereign cloud as part of the journey toward digital sovereignty is more significant now than ever before.

Uncertainty 80

Uncertainty Metadata Machine Learning Data Governance 80

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

BizAcuity

MARCH 18, 2019

Credit scoring systems and predictive analytics model attempt to quantify uncertainty and provide guidance for identifying, measuring and monitoring risk. The consumer lending business is centered on the notion of managing the risk of borrower default. Benefits of Predictive Analytics in Unsecured Consumer Loan Industry.

Predictive Analytics 83

Predictive Analytics Analytics Cost-Benefit Data mining 83

CIO Business Intelligence

JANUARY 4, 2023

Moving forward, we will see workflows that are more capable and widely adopted to facilitate edge-core-cloud needs like generating meshes, performing 3D simulations, performing post-simulation data analysis, and feeding data into machine learning models—which support, guide, and in some case replace the need for simulation.

Cost-Benefit 57

Cost-Benefit Machine Learning Modeling Uncertainty 57

CIO Business Intelligence

SEPTEMBER 12, 2022

One instance of how that exploration led to real business benefits was with the application of machine learning to predict optimal product formulation using a set of desired consumer benefits. The team was given time to gather and clean data and experiment with machine learning models,’’ Crowe says.

Experimentation 122

Experimentation Consulting Technology Machine Learning 122

Domino Data Lab

JUNE 2, 2019

Secondly, I talked backstage with Michelle, who got into the field by working on machine learning projects, though recently she led data infrastructure supporting data science teams. Just doing machine learning is not enough, and sometimes not even necessary.”. First off, her slides are fantastic! Nick Elprin.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

In conferences and research publications, there is a lot of excitement these days about machine learning methods and forecast automation that can scale across many time series. This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. Forecasting at the “push of a button”?

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

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

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. Hollywood is a $10 billion-a-year industry, and movies range from huge hits to box office bombs.

Risk 67

Risk ROI Modeling Metrics 67

Sisense

SEPTEMBER 9, 2019

Then we ran Kraken’s machine learning and predictive modeling engine to get the results. Following a few false starts and some great iterative learning with the BigSquid team, we came away with a solid predictive data model of the warranty costs for future periods.

Predictive Analytics 75

Predictive Analytics Predictive Modeling Forecasting Data-driven 75

Cloudera

NOVEMBER 19, 2018

One of Cloudera’s partners offers “Sustainability Services” with a goal of assisting organizations in turning costs and risks associated with changing regulatory and workforce environments, as well as supply chain uncertainties and volatile markets, into business opportunities.

Digital Transformation 40

Digital Transformation IoT Cost-Benefit Uncertainty 40

IBM Big Data Hub

APRIL 18, 2024

Beyond cost savings, organizations seek tangible ways to measure gen AI’s return on investment (ROI), focusing on factors like revenue generation, cost savings, efficiency gains and accuracy improvements, depending on the use case. The AGI would need to handle uncertainty and make decisions with incomplete information.

Cost-Benefit 91

Cost-Benefit Modeling Manufacturing Interactive 91

O'Reilly on Data

JULY 20, 2021

This measurement of trust and risk is benefited by understanding who could be in front of the device. We can’t forget that the machine learning that is doing biometrics is not a deterministic calculation; there is always some degree of uncertainty.

Risk 303

Risk Software Interactive Modeling 303

Sisense

JULY 29, 2021

The ability to easily sell rooms to walk-ins and last-minute bookings, upsell current reservations, even utilize a contactless check-in/check-out system are all game-changers for hotels in this age of uncertainty, where changes to travel laws and quarantine rules can happen at any time, throwing municipalities and travel plans into disarray.

Insurance 52

Insurance Marketing Uncertainty Reporting 52

Andrew White

APRIL 22, 2020

Emergency measures are undertaken with little planning. In this second phase executive leaders will need to make critical business decisions with even less data and with more uncertainty. AI and machine learning will help, along with other data and analytics capabilities. Things stop working.

Software 52

Software Uncertainty Measurement Marketing 52

The Unofficial Google Data Science Blog

APRIL 19, 2021

Practical Reason #2: Model Modularity In complex machine learning systems, models depend on each other. The numerical value of the signal became decoupled from the event it was measuring even as the ordinal value remained unchanged. bar{pi} (1 - bar{pi})$: This is the irreducible loss due to uncertainty.

Modeling 122

Modeling IT Metrics Testing 122

The Unofficial Google Data Science Blog

AUGUST 27, 2019

A machine learning classifier serves this task perfectly. The measurement may be biased if our samples are generated from a procedure that samples without replacement, such as reservoir sampling , especially if some items have disproportionate weight, i.e., $p(v_i) cdot n$ is large. High Risk 10% 5% 33.3% How Many Strata?

Metrics 98

Metrics Statistics Uncertainty Optimization 98

bridgei2i

OCTOBER 20, 2021

Slowly and gradually, the industry trend shifted to the online sensors where you don’t have to run around just for the measurement. Considering the current situation, I will not say that, you know, it is a COVID situation, because we don’t know, when COVID will be over.

Manufacturing 98

Manufacturing Enterprise IoT Technology 98