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. Let’s start with the models. And those experiments have paid off.

Experimentation 119

Experimentation Strategy Machine Learning Interactive 119

Smart Data Collective

MARCH 28, 2022

It can be further classified as statistical and predictive modeling, but the two are closely associated with each other. Prescriptive data analytics: It is used to predict outcomes and necessary subsequent actions by combining the features of big data and AI. They can be again classified as random testing and optimization.

Data Analytics 85

Data Analytics Software Analytics Statistics 85

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

DataRobot Blog

JUNE 23, 2022

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. Go Beyond Predictions. Far fewer AI models succeed in creating business value when there is no existing prediction to replace.

Predictive Modeling 59

Predictive Modeling Modeling Data Science Measurement 59

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. He also tests data accuracy and product functionality.

Predictive Modeling 128

Predictive Modeling Visualization Reporting Interactive 128

DataRobot Blog

JULY 22, 2022

Using AI-based models increases your organization’s revenue, improves operational efficiency, and enhances client relationships. You need to know where your deployed models are, what they do, the data they use, the results they produce, and who relies upon their results. That requires a good model governance framework.

Modeling 105

Modeling Risk Management Testing Risk 105

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

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.”

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

DataRobot Blog

DECEMBER 15, 2022

Working from datasets you already have, a Time Series Forecasting model can help you better understand seasonality and cyclical behavior and make future-facing decisions, such as reducing inventory or staff planning. Calendars can also help you understand seasonality and incorporate it into the forecast model.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

CIO Business Intelligence

MARCH 5, 2024

CIOs and IT leaders are at the center and must decide what copilots to test, who should receive access, and whether experiments are delivering business value. Today, top AI-assistant capabilities delivering results include generating code, test cases, and documentation. So, what delivers on the productivity promise today?

Cost-Benefit 112

Cost-Benefit Advertising Experimentation Software 112

Sisense

SEPTEMBER 25, 2020

Interpreting better results: Statistical techniques allow users to make predictions for unseen data, more easily improving the accuracy of output and results. Applying appropriate machine learning (ML) models: Different ML techniques are better suited for different types of problems. Actual Predicted 23.1 24.369364 32.2

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

Alation

APRIL 14, 2022

And, as organizations progress and grow, “data drift” starts to impact data usage, models, and your business. Automatic sampling to test transformation. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. A useful feature for exposing patterns in the data.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Cloudera

SEPTEMBER 7, 2023

With the emergence of new creative AI algorithms like large language models (LLM) fromOpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa, and Bloomberg’s BloombergGPT—awareness, interest and adoption of AI use cases across industries is at an all time high. The reality of LLMs and other “narrow” AI technologies is that none of them is turn-key.

Insurance 79

Insurance Risk Data-driven Finance 79

DataRobot Blog

DECEMBER 1, 2022

Ingest your data and DataRobot will use all these data points to train a model—and once it is deployed, your marketing team will be able to get a prediction to know if a customer is likely to redeem a coupon or not and why. Training and Testing Different AI Models. Automate Feature Engineering.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

Domino Data Lab

AUGUST 22, 2019

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

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. In the image above, we have imported the DataRobot Python client and kickstarted model training using Autopilot, the DataRobot automated machine learning capability. Did you notice?

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

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 57

Strategy Management IoT Cost-Benefit 57

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model. Nor can we learn prediction intervals across a large set of parallel time series, since we are trying to generate intervals for a single global time series.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Domino Data Lab

AUGUST 20, 2019

Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models. It is a framework for approaching ML as well as providing techniques for extracting features from raw data that can be used within the models. We can also modify the features before we start the modeling process.

Testing 68

Testing Modeling Interactive Measurement 68

Domino Data Lab

OCTOBER 23, 2019

This chapter will explore the numbers behind the numbers using ML and then creating an API to serve out the ML model. This means covering details like setting up your environment, deployment, and monitoring, in addition to creating models on clean data. You can add a Makefile command test that will run all of your notebooks by issuing.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

JULY 9, 2019

Assessing whether a business stakeholder is trying to solve for a problem that is descriptive, predictive, or prescriptive and then re-framing the problem as supervised learning, unsupervised learning, or reinforcement learning, respectively. Because I’m an academic I like to look at the original founding documents. Good, okay.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Rocket-Powered Data Science

SEPTEMBER 20, 2021

We would be able to go far beyond searching for correctly spelled column headings in databases or specific keywords in data documentation, to find the data we needed (assuming we even knew the correct labels, metatags, and keywords used by the dataset creators). Sharing and integrating such important data streams has never been such a dream.

Data Science 285

Data Science Forecasting Business Intelligence Sales 285

Occam's Razor

NOVEMBER 18, 2014

Short story #2: Predictive Modeling, Quantifying Cost of Inaction. Short story #2: Predictive Modeling, Quantifying Cost of Inaction. The work of the New York Times team inspired me it to do some predictive modeling for inaction in our world of digital marketing. Thank goodness for predictive models.

Visualization 143

Visualization Slice and Dice Predictive Modeling Metrics 143

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. The model and the data specification become more important than the code. Let’s get everybody to do X.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

datapine

NOVEMBER 27, 2019

However, businesses today want to go further and predictive analytics is another trend to be closely monitored. Another increasing factor in the future of business intelligence is testing AI in a duel. The predictive models, in practice, use mathematical models to predict future happenings, in other words, forecast engines.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

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

Jet Global

SEPTEMBER 1, 2023

Data-Driven Decision Making: Embedded predictive analytics empowers the development team to make informed decisions based on data insights. By integrating predictive models directly into the application, developers can provide real-time recommendations, forecasts, or insights to end-users.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52

Jet Global

MAY 1, 2023

These licensing terms are critical: Perpetual license vs subscription: Subscription is a pay-as-you-go model that provides flexibility as you evaluate a vendor. Pricing model: The pricing scale is dependent on several factors. It is organized to create a top-down model that is used for analysis and reporting.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52