IBM Big Data Hub

MAY 1, 2023

The early versions of AI were capable of predictive modelling (e.g., The four categories of predictive modelling, robotics, speech and image recognition are collectively known as algorithm-based AI or Discriminative AI. AI’s Image recognition can automatically read, interpret, and process documents and images (e.g.,

Insurance 83

Insurance Predictive Modeling Cost-Benefit Statistics 83

IBM Big Data Hub

AUGUST 28, 2023

Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs. Topic modeling: Topic modeling aims to discover underlying themes and/or topics in a collection of documents.

Data mining 58

Data mining Machine Learning Deep Learning Marketing 58

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 143

Deep Learning Machine Learning Modeling Software 143

CIO Business Intelligence

OCTOBER 5, 2023

With hype around generative AI exploding of late, the CBRE team developed a multi-LLM, self-service generative AI platform that enables employees to use gen AI for a range of tasks, such as gaining insights from proprietary data and documents, using chatbots to work through problems, generating new content, and translating forms.

Experimentation 130

Experimentation Strategy Machine Learning Interactive 130

CIO Business Intelligence

DECEMBER 13, 2022

“Our digital transformation strategy is centered around establishing a consumer-oriented model that helps us customize chronic care management based on the ever-changing conditions of each patient.” A lot of innovation initiatives right now are small and more closely aligned with tangible results.

Strategy 98

Strategy Technology Digital Transformation Data Lake 98

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. That’s where model debugging comes in. Interpretable ML models and explainable ML.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

CIO Business Intelligence

DECEMBER 11, 2023

Resource matchmaker At the heart of Smart Resourcing is what Oeler calls a “predictive” model that relates “work to the people and the people back to the work.” Unable to find the right opportunities to exercise their skills, these employees run the risk of being disengaged without the right connections.

Digital Transformation 59

Digital Transformation Predictive Modeling Management Modeling 59

DataRobot Blog

APRIL 1, 2022

In many cases, many models in the pipeline do not ever make it to production, and among those that do, controlling and managing them in different environments can be difficult. Also, degrading models over time can pose a significant risk to the business. I recently gave a talk on this topic at the AI Summit Silicon Valley.

Machine Learning 59

Machine Learning Cost-Benefit Predictive Modeling Modeling 59

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.

Predictive Modeling 59

Predictive Modeling Modeling Data Science Measurement 59

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. they can train their own surrogate model.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

CIO Business Intelligence

MARCH 5, 2024

CIOs must also partner with CISOs, legal, human resources, and business leaders to build awareness of policies and develop a generative AI risk management strategy. Today, top AI-assistant capabilities delivering results include generating code, test cases, and documentation. So, what delivers on the productivity promise today?

Cost-Benefit 125

Cost-Benefit Advertising Experimentation Software 125

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 325

Machine Learning Predictive Modeling Software Modeling 325

DataRobot Blog

JULY 22, 2022

At many organizations, the current framework focuses on the validation and testing of new models, but risk managers and regulators are coming to realize that what happens after model deployment is at least as important. Legacy Models. No predictive model — no matter how well-conceived and built — will work forever.

Modeling 105

Modeling Risk Management Testing Risk 105

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 79

Insurance Risk Data-driven Finance 79

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 247

Machine Learning Testing Modeling Cost-Benefit 247

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

IBM Big Data Hub

DECEMBER 20, 2023

For instance, if data scientists were building a model for tornado forecasting, the input variables might include date, location, temperature, wind flow patterns and more, and the output would be the actual tornado activity recorded for those days. temperature, salary).

Machine Learning 81

Machine Learning Modeling Deep Learning Predictive Modeling 81

Alation

APRIL 14, 2022

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. Problems arise when data sources are semantically incompatible.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Smart Data Collective

MAY 17, 2021

Legacy solutions might have used paper trails and documents, but that same information is now digital. Predictive models, estimates and identified trends can all be sent to the project management team to speed up their decisions. It’s making waves in nearly every industry, including commercial fleet management.

Big Data 100

Big Data Cost-Benefit IoT Machine Learning 100

DataRobot Blog

JANUARY 10, 2023

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. DataRobot Notebooks are fully compatible with the Jupyter Notebook standard, allowing for interoperability with the rest of the ecosystem.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

IBM Big Data Hub

JULY 5, 2023

Reduced risk of security breaches: Through real-time asset tracking and improved asset security capabilities, market-leading ALM systems can now help businesses track and monitor their assets in a way that helps prevent theft and data breaches.

Strategy 57

Strategy Management IoT Cost-Benefit 57

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

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

Cloudera

SEPTEMBER 5, 2018

It provides more relevant matches for KYC screening, enables better predictive algorithms for risk scoring, shortens time spent on investigation, and cuts down on costs to support periodic regulatory reviews. Collecting and evaluating documents to ascertain customer identity. Risk assessment. Watchlist screening.

Machine Learning 40

Machine Learning Risk Cost-Benefit Big Data 40

O'Reilly on Data

SEPTEMBER 11, 2019

So, we used a form of the Term Frequency-Inverse Document Frequency (TF/IDF) technique to identify and rank the top terms in this year’s Strata NY proposal topics—as well as those for 2018, 2017, and 2016. But the database—or, more precisely, the data model —is no longer the sole or, arguably, the primary focus of data engineering.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

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

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. PDPs for the bicycle count prediction model (Molnar, 2009). A14 : no checking account.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

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

Depict Data Studio

SEPTEMBER 17, 2019

His experience includes evaluation and outcomes studies, ROI analysis, IBNR determination, predictive modeling, risk adjustment methodologies, advanced data visualization, dashboard design and implementation, database development and management, and identifying and evaluating trends and forces in data.

Visualization 40

Visualization Consulting Dashboards Reporting 40

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.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

Jet Global

SEPTEMBER 1, 2023

But we’re also seeing its use expand in other industries, like Financial Services applications for credit risk assessment or Human Resources applications to identify employee trends. Using the information from predictive analytics can help companies—and business applications—suggest actions that can affect positive operational changes.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52

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