BizAcuity

JANUARY 18, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. OCR is widely used to digitize all kinds of physical documentation. Predictive Analytics.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

O'Reilly on Data

SEPTEMBER 19, 2023

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.” A single document may represent thousands of features. “Here’s our risk model.

Machine Learning 243

Machine Learning Testing Modeling Cost-Benefit 243

O'Reilly on Data

JANUARY 17, 2023

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

Machine Learning 321

Machine Learning Predictive Modeling Software Modeling 321

O'Reilly on Data

DECEMBER 12, 2019

Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1] 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems.

Machine Learning 187

Machine Learning Modeling Testing Risk Management 187

CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software.

Deep Learning 142

Deep Learning Machine Learning Modeling Software 142

CIO Business Intelligence

OCTOBER 5, 2023

As chief digital and technology officer at CBRE, Davé recognized early that the commercial real estate industry was ripe for AI and machine learning enhancements, and he and his team have tested countless use cases across the enterprise ever since. For AI, the high-value quadrant is where you’ll find most predictive modeling.

Experimentation 113

Experimentation Strategy Machine Learning Interactive 113

Cloudera

SEPTEMBER 7, 2023

But it’s also fraught with risk. This June, for example, the European Union (EU) passed the world’s first regulatory framework for AI, the AI Act , which categorizes AI applications into “banned practices,” “high-risk systems,” and “other AI systems,” with stringent assessment requirements for “high-risk” AI systems.

Insurance 81

Insurance Risk Data-driven Finance 81

Sisense

SEPTEMBER 25, 2020

Linear regression is a form of supervised learning (or predictive modeling). In supervised learning, the dependent variable is predicted from the combination of independent variables. When a single independent variable is used to predict the value of a dependent variable, it’s called simple linear regression. Clustering.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

datapine

NOVEMBER 27, 2019

This is one of the major trends chosen by Gartner in their 2020 Strategic Technology Trends report , combining AI with autonomous things and hyperautomation, and concentrating on the level of security in which AI risks of developing vulnerable points of attacks. Industries harness predictive analytics in different ways.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

MARCH 24, 2020

John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing. The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data. Let’s get everybody to do X.

Machine Learning 343

Machine Learning Statistics Data Quality Modeling 343

Alation

APRIL 14, 2022

Automatic sampling to test transformation. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. These factors risk data originating in far-flung environments, where the data structures and semantics are not well understood or documented. Visual Profiling.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

DataRobot Blog

DECEMBER 1, 2022

Training and Testing Different AI Models. As DataRobot starts building predictive models, a large repository of open source and proprietary packages will experiment with various modeling techniques. The model selection process will test several models to see which one is likely to yield the best results.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

DataRobot Blog

JUNE 23, 2022

Your artificial intelligence (AI) system has given you this “predicted feature” in addition to what you already know about the applicant. Business rules set by the credit committee to control business risk and the loan portfolio also constrain accepting this application and advancing the loan. Automate and Scale Your Decisions.

Predictive Modeling 59

Predictive Modeling Modeling Data Science Measurement 59

IBM Big Data Hub

JULY 5, 2023

It allows the company to run tests and predict performance based on simulations. With a good digital twin, it’s possible to predict how an asset will perform under certain conditions and what it’s projected lifespan will be. A digital twin is a virtual representation of an asset that a company intends to purchase.

Strategy 53

Strategy Management IoT Cost-Benefit 53

Domino Data Lab

AUGUST 1, 2021

This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. A comprehensive list of all attributes and symbol codes is given in the document that accompanies the original dataset. After forming the X and y variables, we split the data into training and test sets.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

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. Clinics and hospitals like Phoenix Children’s use AI to predict which patients are at risk of contracting an illness so that they can then prescribe medication and treatment accordingly.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Done right, strategic forecasts can provide insights to decision makers on trends, incorporate forward-looking knowledge of product plans and technology roadmaps when relevant, expose the risks and biases of relying on any one forecasting methodology, and invite input from stakeholders on the uncertainty ranges.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Jet Global

MAY 1, 2023

CEO Priorities Grow revenue and “hit the number” Manage costs and meet profitability goals Attract and retain talent Innovate and out-perform the competition Manage risk Connect the Dots Present embedded analytics as a way to differentiate from the competition and increase revenue. Present your business case.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Depict Data Studio

SEPTEMBER 17, 2019

You’ll also learn about 508 compliance, like how to test your visuals for sufficient foreground/background contrast, colorblindness legibility, and grayscale legibility using accessibility checkers. . Her interests lie in improving evaluation communication to increase use through data visualization, data storytelling, and document design.

Visualization 40

Visualization Consulting Dashboards Reporting 40