CIO Business Intelligence

MARCH 23, 2023

As the world continues to experience economic uncertainty, IT leaders look to tighten budgets, consolidate tools and resources, and generally become more risk-averse when evaluating new investments. Consider your company scorecard: Your procurement team has a scorecard with clear metrics to evaluate purchase decisions. to just 2.2%

Scorecard 59

Scorecard Cost-Benefit ROI IT 59

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

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 87

Forecasting Data Warehouse Machine Learning Modeling 87

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 59

Cost-Benefit Machine Learning Modeling Uncertainty 59

Domino Data Lab

JULY 22, 2019

why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”. But for most enterprise, using machine learning…not really.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

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. Here, $X$ is a vector of tuning parameters that control the system's operating characteristics (e.g.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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. Monitoring.

Management 246

Management Metrics Machine Learning Modeling 246

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. Moreover, advanced metrics like Percentage Regional Sales Growth can provide nuanced insights into business performance. One of the primary sources of tension?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

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

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. . Demand transparency on everything — especially on metrics — and be transparent back.

IT 133

IT Cost-Benefit Uncertainty Digital Transformation 133

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

AUGUST 27, 2019

Of course, any mistakes by the reviewers would propagate to the accuracy of the metrics, and the metrics calculation should take into account human errors. If we could separate bad videos from good videos perfectly, we could simply calculate the metrics directly without sampling. The missing verdicts create two problems.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

The Unofficial Google Data Science Blog

JULY 18, 2023

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. We will follow the example of Janson and Olsson , and start from this generalized definition of the metric, which they call iota.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

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

The Unofficial Google Data Science Blog

APRIL 19, 2021

Practical Reason #2: Model Modularity In complex machine learning systems, models depend on each other. Calibration and other considerations Calibration is a desirable property, but it is not the only important metric. bar{pi} (1 - bar{pi})$: This is the irreducible loss due to uncertainty.

Modeling 122

Modeling IT Metrics Testing 122

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

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

For example, in our field, we can generally blame machine learning feedback (predictions that change the data itself), budget effects (bidders running out of money in repeated auctions) or even the weather (internet usage changes in complicated ways).

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45