The Unofficial Google Data Science Blog

DECEMBER 4, 2019

ln this post he describes where and how having “humans in the loop” in forecasting makes sense, and reflects on past failures and successes that have led him to this perspective. Our team does a lot of forecasting. It also owns Google’s internal time series forecasting platform described in an earlier blog post.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Domino Data Lab

AUGUST 9, 2021

For a model-driven enterprise, having access to the appropriate tools can mean the difference between operating at a loss with a string of late projects lingering ahead of you or exceeding productivity and profitability forecasts. What Are Modeling Tools? Importance of Modeling Tools. Types of Modeling Tools.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

CIO Business Intelligence

FEBRUARY 2, 2024

A PwC report, for instance, found that 84% of CIOs expect to use genAI to support a new business model in 2024. Predictive AI: This technology is forward-looking, analyzing past data to unearth predictive patterns and then using current data to provide accurate forecasts of what will happen in the future.

Testing 142

Testing Risk Forecasting Data-driven 142

CIO Business Intelligence

FEBRUARY 5, 2024

A PwC report, for instance, found that 84% of CIOs expect to use genAI to support a new business model in 2024. Predictive AI: This technology is forward-looking, analyzing past data to unearth predictive patterns and then using current data to provide accurate forecasts of what will happen in the future.

Testing 129

Testing Risk Forecasting Data-driven 129

CIO Business Intelligence

JANUARY 19, 2024

The US Bureau of Labor Statistics (BLS) forecasts employment of data scientists will grow 35% from 2022 to 2032, with about 17,000 openings projected on average each year. Candidates for the exam are tested on ML, AI solutions, NLP, computer vision, and predictive analytics.

Data Science 124

Data Science Machine Learning Predictive Modeling Experimentation 124

Rocket-Powered Data Science

SEPTEMBER 25, 2022

This is all fueled and facilitated by data flows across processes, products, and people’s activities, used in synergy with computational models and simulations of the system being digitally twinned. All phases of the MVT process are discussed: strategy, designs, pilot, implementation, test, validation, operations, and monitoring.

Diagnostic Analytics 216

Diagnostic Analytics Prescriptive Analytics Internet of Things IoT 216

Jen Stirrup

FEBRUARY 9, 2024

For example, India is also using AI to enhance weather forecasting and climate modelling. The Indian government is testing AI-powered climate models to improve weather forecasts across the country [3]. Artificial Intelligence is one way forward to tackling the issues of climate change.

Forecasting 105

Forecasting Measurement Testing Technology 105

O'Reilly on Data

NOVEMBER 28, 2023

And everyone has opinions about how these language models and art generation programs are going to change the nature of work, usher in the singularity, or perhaps even doom the human race. 16% of respondents working with AI are using open source models. A few have even tried out Bard or Claude, or run LLaMA 1 on their laptop.

Enterprise 267

Enterprise Modeling Testing Reporting 267

DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

Rocket-Powered Data Science

SEPTEMBER 20, 2021

The data scientists need to find the right data as inputs for their models — they also need a place to write-back the outputs of their models to the data repository for other users to access. The BI team may be focused on KPIs, forecasts, trends, and decision-support insights. That’s data democratization.

Data Science 286

Data Science Forecasting Business Intelligence Sales 286

Cloudera

JUNE 29, 2023

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices.

Data Architecture 84

Data Architecture Risk Management Risk Management 84

CIO Business Intelligence

APRIL 23, 2024

Once you’ve identified the SAFe training and certification providers that meet your goals, you’ll need to review the materials, study guides, and take any available practice tests. The 45-question exam tests candidates’ ability to: Facilitate Scrum events. Use design thinking to achieve desirable, feasible, and sustainable products.

Consulting 83

Consulting Testing Software Forecasting 83

The Unofficial Google Data Science Blog

APRIL 19, 2021

by LEE RICHARDSON & TAYLOR POSPISIL Calibrated models make probabilistic predictions that match real world probabilities. To explain, let’s borrow a quote from Nate Silver’s The Signal and the Noise : One of the most important tests of a forecast — I would argue that it is the single most important one — is called calibration.

Modeling 122

Modeling IT Metrics Testing 122

CIO Business Intelligence

SEPTEMBER 7, 2023

The first is forecasting, where AI is used to make predictions about downstream demand or upstream shortages. In the meantime, many companies continue to reap the benefits of improved forecasting and inspection. Some of the challenges Amcor faces in manufacturing have to do with accurate forecasting and adapting to changing demand.

Management 136

Management Forecasting Manufacturing Cost-Benefit 136

Smarten

JULY 23, 2019

Put simply, predictive analytics is a method used to forecast and predict the future results and needs of an organization using historical data and a comprehensive set of data from across and outside the enterprise. Predictive Modeling allows users to test theories and hypotheses and develop the best strategy.

Modeling 63

Modeling Predictive Analytics Predictive Modeling IT 63

Smart Data Collective

JUNE 15, 2021

However, it is important to make sure that you understand the potential role of AI and what business model to build around it. However, even the most brilliant idea built around AI technology can fail without a proper business model. Without a good business model, you won’t understand customer needs and how to build your startup.

Modeling 91

Modeling Advertising Cost-Benefit Machine Learning 91

AWS Big Data

OCTOBER 26, 2023

Data analysts and database developers want to use this data to train machine learning (ML) models, which can then be used to generate insights on new data for use cases such as forecasting revenue, predicting customer churn, and detecting anomalies. We also need an offline feature store to store features in feature groups.

Modeling 62

Modeling Data Warehouse Machine Learning Testing 62

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. You must detect when the model has become stale, and retrain it as necessary. The Core Responsibilities of the AI Product Manager. The AI Product Development Process.

Marketing 361

Marketing Experimentation Metrics Testing 361

CIO Business Intelligence

JULY 5, 2022

Research firm Gartner defines business analytics as “solutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states.”. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 119

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 119

Corinium

JUNE 6, 2019

Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments. It is also important to have a strong test and learn culture to encourage rapid experimentation. Therefore, understanding customers for cross and up-sell is paramount.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

DataRobot

OCTOBER 19, 2021

Many available forecasts provide less than four weeks notice at the state and county level. Additionally, these forecasts often miss surges in community transmission until it is too late to change course. Traditional predictive models do not account for anomaly detection on data reporting issues (e.g., reporting backlogs).

Forecasting 98

Forecasting Predictive Modeling Optimization Testing 98

Smart Data Collective

JANUARY 27, 2021

A number of new predictive analytics algorithms are making it easier to forecast price movements in the cryptocurrency market. Conversely, if predictive analytics models suggest that the value of a cryptocurrency price is likely to decrease, more investors are likely to sell off their cryptocurrency holdings.

Machine Learning 138

Machine Learning Forecasting Predictive Analytics Marketing 138

CIO Business Intelligence

DECEMBER 11, 2023

For example, by tapping into real-time data with AI-enabled analytics, CFOs will be able to develop multiple scenarios for capital allocation, offering more forward-looking projections and more accurate forecasts.

Finance 85

Finance Forecasting IoT Predictive Modeling 85

Domino Data Lab

MARCH 3, 2021

It’s often stated that nothing changes inside an enterprise because you’ve built a model. In some cases, data science does generate models directly to revenue, such as a contextual deal engine that targets people with offers that they can instantly redeem. But what about good decisions?

Metrics 93

Metrics Modeling Sales Data Science 93

IBM Big Data Hub

FEBRUARY 13, 2024

This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task. Generative adversarial networks (GANs) or variational autoencoders (VAEs) are used for images, videos, 3D models and music. Autoregressive models or large language models (LLMs) are used for text and language.

Enterprise 120

Enterprise Modeling Testing Data-driven 120

Smarten

JUNE 26, 2018

This article provides a brief explanation of the ARIMA method of analytical forecasting. What is ARIMA Forecasting? This analytical forecasting method is suitable for instances when data is stationary/non stationary and is univariate, with any type of data pattern, i.e., level/trend/seasonality/cyclicity. About Smarten.

Forecasting 40

Forecasting Enterprise IT Sales 40

Smart Data Collective

OCTOBER 8, 2021

Modern machine learning and back-testing; how quant hedge funds use it. Similarly, hedge funds often use modern machine learning and back-testing to analyze their quant models. Here, the models get tested using historical data to evaluate their profitability. Mathematical Model-based Strategies.

Machine Learning 113

Machine Learning Testing Strategy Modeling 113

IBM Big Data Hub

JANUARY 26, 2024

A user-first approach to climate tech Net Zero Atlantic’s open source Atlantic Canada Energy System (ACES) Model is a powerful tool for analyzing and forecasting energy generation and infrastructure development scenarios. “It’s usually the other way around. “One of our key roles is to engage community.

Testing 112

Testing Modeling Forecasting Technology 112

Smarten

JUNE 29, 2018

This article looks at the ARIMAX Forecasting method of analysis and how it can be used for business analysis. What is ARIMAX Forecasting? This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity. About Smarten.

Forecasting 40

Forecasting Enterprise IT Business Intelligence 40

datapine

MAY 2, 2023

Your Chance: Want to test a professional logistics analytics software? In a recent move towards a more autonomous logistical future, Amazon has launched an upgraded model of its highly-successful KIVA robots. Your Chance: Want to test a professional logistics analytics software? Where is all of that data going to come from?

Big Data 275

Big Data Cost-Benefit Internet of Things Optimization 275

CIO Business Intelligence

JULY 18, 2023

It was when Nvidia reported strong results for the three months to April 30, 2023, and forecast that its sales could jump by 50% in the following fiscal quarter, that its stock market valuation soared, catapulting it into the exclusive trillion-dollar club alongside well-known tech giants Alphabet, Amazon, Apple, and Microsoft.

Manufacturing 91

Manufacturing Forecasting Software Marketing 91

DataRobot Blog

AUGUST 18, 2022

Challenges in inventory management, demand forecasting, price optimization, and more can result in missed opportunities and lost revenue. Retailers and manufacturers that incorporate AI in supply chain management greatly enhance their ability to forecast demand, manage inventory, and optimize price. Francois Fressin.

Manufacturing 98

Manufacturing Cost-Benefit Forecasting Data-driven 98

CIO Business Intelligence

MAY 1, 2024

Gen AI takes us from single-use models of machine learning (ML) to AI tools that promise to be a platform with uses in many areas, but you still need to validate they’re appropriate for the problems you want solved, and that your users know how to use gen AI effectively.

Cost-Benefit 134

Cost-Benefit Metrics Insurance Measurement 134

DataRobot

OCTOBER 18, 2021

They designed experiments that tested real-life human behaviors against those expected from economic theory. There is a wealth of research showing again and again that evidence-based algorithms are more accurate than forecasts made by humans. Haggling isn’t the only way that humans don’t behave the way that economic theory suggests.

Forecasting 145

Forecasting Digital Transformation Cost-Benefit Data-driven 145

CIO Business Intelligence

MAY 14, 2024

Enterprises need to ensure that private corporate data does not find itself inside a public AI model,” McCarthy says. You don’t want a mistake to happen and have it end up ingested or part of someone else’s model. The excitement and related fears surrounding AI only reinforces the need for private clouds.

IT 135

IT Data Processing Enterprise Modeling 135

AWS Big Data

FEBRUARY 1, 2023

Storage and redundancy – Due to the heterogeneous data stores and models, it was challenging to store the different datasets from various business stakeholder teams. This will make launching and testing models simpler. The response times for these data sources are critical to our key stakeholders.

Optimization 52

Optimization Forecasting Data Lake Metadata 52