IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Foundation models can use language, vision and more to affect the real world. Foundation models can apply what they learn from one situation to another through self-supervised and transfer learning.

Risk 77

Risk Modeling Management Metadata 77

John Wheeler

OCTOBER 20, 2020

Risk & Control Documentation /Assessment. Risk statements and the related controls required to mitigate them to an acceptable level must be documented sufficiently to satisfy key internal and external stakeholders — including regulators, external auditors, business partners/associates, suppliers, senior executives and board members.

Risk Management 52

Risk Management Risk Predictive Modeling Insurance 52

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 57

Data mining Machine Learning Deep Learning Marketing 57

Smarten

NOVEMBER 8, 2022

It provides an individual study environment that includes video, slides, lectures and supporting documentation for further study and reference. It is also suitable for those that wish to find out more about the Citizen Data Scientist approach to Data Literacy and fact-based decision-making.

Business Intelligence 52

Business Intelligence Predictive Modeling Statistics Data-driven 52

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 119

Experimentation Strategy Machine Learning Interactive 119

DataRobot Blog

APRIL 29, 2022

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. When business decisions are made based on bad models, the consequences can be severe.

Risk 111

Risk Modeling IT Risk Management 111

Juice Analytics

MARCH 11, 2021

Predictive models to take descriptive data and attempt to tell the future. Documentation — Because data products live on and touch many people within your organization. She's responsible for documenting business logic as product decisions are made. How are you going to turn that data into a solution?

Predictive Modeling 128

Predictive Modeling Visualization Reporting Interactive 128

Smart Data Collective

MARCH 28, 2022

There are four main types of data analytics: Predictive data analytics: It is used to identify various trends, causation, and correlations. It can be further classified as statistical and predictive modeling, but the two are closely associated with each other.

Data Analytics 85

Data Analytics Software Analytics Statistics 85

Ontotext

MAY 25, 2023

The Master class that Peio delivered covered Reconciliation, Text Analytics and Virtualization with Knowledge Graphs, using GraphDB and Text Analysis services tailored explicitly for tasks demanding intricate domain knowledge and linking documents to reference or master data.

Machine Learning 52

Machine Learning Structured Data Unstructured Data Predictive Modeling 52

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 94

Strategy Technology Digital Transformation Data Lake 94

Sisense

MARCH 7, 2021

The app not only documents utilization data, but allows users to add subjective inputs such as personal preferences. Since those early days, Measuremen has broadened its data sources. A mobile app is used to collect granular information about how each component or meeting space is used.

Slice and Dice 98

Slice and Dice Data-driven Cost-Benefit Forecasting 98

Smart Data Collective

APRIL 10, 2020

By digitizing old documents and going paperless in your day to day operations, you will not only see direct savings but will also help your employees become more productive in the process.

Big Data 138

Big Data Cost-Benefit Metrics Marketing 138

O'Reilly on Data

JUNE 18, 2019

Moreover, the domain knowledge, which often is not encoded in the data (nor fully documented), is an integral part of this data (see this article from Forbes). In a nutshell, data programming techniques provide ways to “manufacture” data that we can feed to various learning and predictions tasks (even for ML data quality solutions).

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

DataRobot Blog

JUNE 23, 2022

Most successful ML and AI projects are replacing an existing prediction with a more accurate and/or faster one. Human intuition is relied on, or an older, hand-build BI/predictive model exists, or perhaps a set of heuristics were documented that more or less predict something.

Predictive Modeling 59

Predictive Modeling Modeling Data Science Measurement 59

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.” Pendlebury points to the example of a large document review task that came in requiring a “small army of associates.”

Digital Transformation 59

Digital Transformation Predictive Modeling Management Modeling 59

DataRobot Blog

APRIL 1, 2022

We have a dedicated Trusted AI group working to make sure that the platform supports organizations with this challenge by protecting sensitive features for bias and fairness in development and production, bias mitigation, managing humility rules, access control, workflow process, and auto-generated documentation. Challenge #3.

Machine Learning 59

Machine Learning Cost-Benefit Predictive Modeling Modeling 59

O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

O'Reilly on Data

DECEMBER 12, 2019

Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8].

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Ms SQL Girl

MAY 19, 2016

To simplify, “In-database Advanced Analytics”: you can run powerful statistical / predictive modelling (from R) inside SQL Server. The Getting Started document says that you will need to run installpackages.r. Read the official definition here. Check Out SQL Latest Bits. This is an alternative to R Studio.

Visualization 45

Visualization Predictive Modeling Statistics Reporting 45

Ms SQL Girl

MAY 19, 2016

To simplify, “In-database Advanced Analytics”: you can run powerful statistical / predictive modelling (from R) inside SQL Server. The Getting Started document says that you will need to run installpackages.r. Read the official definition here. Check Out SQL Latest Bits. This is an alternative to R Studio.

Visualization 40

Visualization Predictive Modeling Statistics Reporting 40

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

AWS Big Data

MARCH 7, 2023

Refer to the respective service documentation for further details. These are just some of the KPIs and metrics that are key for predictive modeling of events as the game acquires new players while keeping existing users involved, engaged, and playing. Data subscription and access is fully managed with this service.

Analytics 69

Analytics Data Warehouse Data Lake Metadata 69

CIO Business Intelligence

MARCH 5, 2024

Summarizing meetings, emails, and documents Microsoft 365 will set the benchmark on copilot capabilities, given the speed at which it introduced embedded AI assistant features and the platform’s wide user base. Today, top AI-assistant capabilities delivering results include generating code, test cases, and documentation.

Cost-Benefit 112

Cost-Benefit Advertising Experimentation Software 112

Smart Data Collective

SEPTEMBER 26, 2021

Data visualization enables you to: Make sense of the distributional characteristics of variables Easily identify data entry issues Choose suitable variables for data analysis Assess the outcome of predictive models Communicate the results to those interested. A NoSQl database can use documents for the storage and retrieval of data.

Metadata 99

Metadata Visualization Unstructured Data Data mining 99

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

Rocket-Powered Data Science

FEBRUARY 12, 2021

Repetition implies that the same steps are repeated many times, for example claims processing or business form completion or invoice processing or invoice submission or more data-specific activities, such as data extraction from documents (such as PDFs), data entry, data validation, and report preparation.

ROI 233

ROI Digital Transformation Reporting Machine Learning 233

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

IBM Big Data Hub

DECEMBER 20, 2023

Unsupervised machine learning Unsupervised learning algorithms—like Apriori, Gaussian Mixture Models (GMMs) and principal component analysis (PCA)—draw inferences from unlabeled datasets, facilitating exploratory data analysis and enabling pattern recognition and predictive modeling.

Machine Learning 81

Machine Learning Modeling Deep Learning Predictive Modeling 81

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.

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

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 101

Big Data Cost-Benefit IoT Machine Learning 101

DataRobot Blog

DECEMBER 15, 2022

While AI-powered forecasting can help retailers implement sales and demand forecasting—this process is very complex, and even highly data-driven companies face key challenges: Scale: Thousands of item combinations make it difficult to manually build predictive models. DataRobot Blueprint—from data to predictions.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

Cloudera

SEPTEMBER 7, 2023

Use NLP to analyze and break down regulatory documents, translating complex legal jargon into actionable tasks. Leverage ML/AI to refine risk models, incorporating data from diverse sources, and predicting outcomes based on market sentiment, climate data, etc. Simplify regulatory compliance. AI-ify risk management.

Insurance 79

Insurance Risk Data-driven Finance 79

IBM Big Data Hub

JULY 5, 2023

Until recently, managers and technicians relied on manual tools like Excel spreadsheets and document tracking to accomplish this. This information is then used to build predictive models of asset performance over time and help spot potential problems before they arise.

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

DataRobot Blog

JANUARY 10, 2023

Now that we have set up the notebook environment, let’s explore the capabilities available in DataRobot Notebooks while walking through the process of training and deploying a readmissions prediction model end-to-end. These DataRobot features are huge time savers.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Domino Data Lab

AUGUST 12, 2021

In a previous blog , we have covered how Pandas Profiling can supercharge the data exploration required to bring our data into a predictive modelling phase. For data, this refinement includes doing some cleaning and manipulations that provide a better understanding of the information that we are dealing with. It supports code exports.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126