CIO Business Intelligence

FEBRUARY 14, 2024

That’s not just about the cost of preparing a larger data set than you need, which takes expertise that’s still uncommon and commands a high salary, but also what you’re teaching the model. The same issues were revealed when Microsoft launched Delve, and before that when the FAST integration brought powerful search to SharePoint in 2010.

IT 143

IT Metadata Data-driven Management 143

Jen Stirrup

FEBRUARY 1, 2023

The effects of AI will be magnified in the coming decade as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning.

Recreation/Entertainment 63

Recreation/Entertainment Testing Advertising Insurance 63

CIO Business Intelligence

APRIL 24, 2023

Generative AI (Artificial Intelligence) and its underlying foundation models represent a paradigm shift in innovation, significantly impacting enterprises exploring AI applications. These foundation models are trained on vast amounts of data. The model can then predict or classify new, previously unseen data.

Enterprise 52

Enterprise Deep Learning Modeling Machine Learning 52

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 226

Modeling Machine Learning Predictive Modeling Consulting 226

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. Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for Enterprise Repository Era” (1990–2010) – first generation DG solutions.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

FineReport

APRIL 26, 2023

For example, a computer manufacturing company could develop new models or add features to products that are in high demand. With Big Data Analytics, businesses can make better and quicker decisions, model and forecast future events, and enhance their Business Intelligence. How to Choose the Right Big Data Analytics Tools?

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

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 254

Machine Learning Testing Modeling Cost-Benefit 254

CIO Business Intelligence

FEBRUARY 26, 2024

Organizations have implemented a variety of workforce models over the last two decades or so, but each has eventually proved to leave them with more questions than answers. Fast forward to today.

Cost-Benefit 98

Cost-Benefit Measurement Modeling Strategy 98

O'Reilly on Data

JULY 20, 2021

When we’re building shared devices with a user model, that model quickly runs into limitations. That model doesn’t fit reality: the identity of a communal device isn’t a single person, but everyone who can interact with it. When we consider the risk associated with an action, we need to understand its privacy implications.

Risk 306

Risk Software Interactive Modeling 306

CIO Business Intelligence

JUNE 13, 2023

As chief digital officer of Kotak Mahindra Bank, Deepak Sharma has been instrumental in driving the bank’s digital transformation, future-ready initiatives, and business model innovation strategies. As I was setting up the NRI banking and remittance platform in 2010, I started getting involved with technology.

IT 105

IT Digital Transformation Uncertainty Risk Management 105

Octopai

JUNE 16, 2021

Do you ever feel like taking risks? . If you’re a bank, however, taking risks doesn’t just have implications for you, but for all your customers and (if you’re big enough) for the economy as a whole. . Basel III was drafted in 2010, with finalization and implementation set to take place over the next several years. Market risk.

Management 52

Management Risk Management Risk Visualization 52

CIO Business Intelligence

MAY 9, 2022

By 2010, Netflix had grabbed a 20% share of North American viewership while Blockbuster was delisted from the New York Stock Exchange and filed for bankruptcy. Risk management practices such as in-person meetings for underwriting, determining creditworthiness, and signing loan documents shifted to online channels. It was ironic.

Digital Transformation 98

Digital Transformation Insurance Interactive Risk Management 98

Ontotext

DECEMBER 30, 2022

We have exciting success stories, including the first and popular mission critical implementation of knowledge graphs – BBC’s website for the FIFA world cup in 2010. Of course, there is still heavy lifting needed to lower the cost, risk and time-to-value of such an advanced knowledge management technology.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

Ontotext

DECEMBER 30, 2022

We have exciting success stories, including the first and popular mission critical implementation of knowledge graphs – BBC’s website for the FIFA world cup in 2010. Of course, there is still heavy lifting needed to lower the cost, risk and time-to-value of advanced knowledge management technology.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. In practice, one may want to use more complex models to make these estimates.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

AUGUST 27, 2019

To demonstrate this issue, consider the following toy example of near-optimal importance sampling with categorical scores: Score Prevalence Population Proportion Sample Proportion Low Risk 0.1% High Risk 10% 5% 33.3% Statistics in Biopharmaceutical Research, 2010. [4] 4] William Cochran (1977). Sampling Techniques pp 132-134. [5]

Metrics 98

Metrics Statistics Uncertainty Optimization 98

Occam's Razor

AUGUST 28, 2017

The team at Google adopted the strategy of having a robot learn own its own (rather than programming it with pre-configured models). 415 million diabetic patients worldwide are at risk of Diabetic Retinopathy (DR) – the fastest growing cause of blindness. The one-handed robots in this case had to learn to pick up objects. The problem?

Deep Learning 139

Deep Learning Machine Learning Marketing Strategy 139

Occam's Razor

MARCH 23, 2010

Yet case studies in some sense reduced risk, even if they were simply over blown marketing fluff written by the vendor. There is such little risk to actually trying. As of 2010 I still have a lot more years that I spend in the traditional data warehousing / business intelligence world than in web analytics. Neither do you.

Analytics 118

Analytics Consulting Metrics Experimentation 118

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Selection bias and survivorship bias When we slice a population by some factor or combination of factors and compute a metric over the new sub-populations, we are implicitly specifying a model (the metric is a function of the slicing factors) and testing a hypothesis. Consistency. References [1] Efron, B. 2] Palese P, Wang TT.

Experimentation 61

Experimentation Testing Statistics Metrics 61

O'Reilly on Data

JANUARY 25, 2021

Observability” risks becoming the new name for monitoring. Models are neither code nor data, and we don’t have adequate tools for versioning models either (though tools like MLflow are a start). Frequent deployment assumes that the software can be built relatively quickly, but training a model can take days.

Machine Learning 363

Machine Learning Software Testing Technology 363