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 364

Machine Learning Statistics Data Quality Data Collection 364

O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. All predictive models are wrong at times?—just

Risk 364

Risk Modeling Data Processing Reporting 364

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 semantic layer bridges the gaps between the data cloud, the decision-makers, and the data science modelers.

Data Science 286

Data Science Forecasting Business Intelligence Sales 286

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

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. Just don't fall for their bashing of all other vendors or their silly claims, false, of "superiority" in terms of running 19 billion combinations of tests or the bonus feature of helping you into your underwear each morning. And I meant every word of it.

Testing 113

Testing Experimentation Risk Optimization 113

DataKitchen

SEPTEMBER 21, 2023

In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets. Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

O'Reilly on Data

SEPTEMBER 15, 2020

In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. Outputs from trained AI models include numbers (continuous or discrete), categories or classes (e.g., spam or not-spam), probabilities, groups/segments, or a sequence (e.g.,

Advertising 362

Advertising Cost-Benefit ROI Machine Learning 362

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

Cloudera

DECEMBER 4, 2024

To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput. The Cloudera AI Inference service is a highly scalable, secure, and high-performance deployment environment for serving production AI models and related applications.

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

Cloudera

OCTOBER 14, 2020

Not many other industries have such a sophisticated business model that encompasses a culture of streamlined supply chains, predictive maintenance, and unwavering customer satisfaction. Step 1: Using the training data to create a model/classifier. Fig 2: Diagram showing how CML is used to build ML training models.

Machine Learning 97

Machine Learning Modeling Cost-Benefit Testing 97

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. As machine learning advances globally, we can only expect the focus on model risk to continue to increase.

Risk 111

Risk Modeling IT Risk Management 111

CIO Business Intelligence

JULY 5, 2022

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. 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 136

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 136

CIO Business Intelligence

JANUARY 19, 2024

The exam tests general knowledge of the platform and applies to multiple roles, including administrator, developer, data analyst, data engineer, data scientist, and system architect. Candidates for the exam are tested on ML, AI solutions, NLP, computer vision, and predictive analytics.

Data Science 126

Data Science Machine Learning Predictive Modeling Forecasting 126

CIO Business Intelligence

JUNE 14, 2023

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. Organization: AWS Price: US$300 How to prepare: Amazon offers free exam guides, sample questions, practice tests, and digital training.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

Insight

AUGUST 5, 2020

This created a summary features matrix of 7472 recordings x 176 summary features, which was used for training emotion label prediction models. Prediction models An Exploratory Data Analysis showed improved performance was dependent on gender and emotion. up to 20% for prediction of ‘happy’ in females?

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 98

Smart Data Collective

MARCH 23, 2022

Software that has bugs need to be properly tested at all stages of development for both functionalities as well as cybersecurity. The industry has also shown that the automation of software testing and especially cyber security testing, results in systems that are more reliable and safer.

Internet of Things 131

Internet of Things IoT Cost-Benefit Testing 131

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 64

Modeling Predictive Analytics Predictive Modeling IT 64

Domino Data Lab

OCTOBER 7, 2020

MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. Statistics developed in the last century are based on probability models (distributions). The accuracy of any predictive model approaches 100%.

Statistics 145

Statistics Testing Predictive Modeling Big Data 145

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

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

Domino Data Lab

NOVEMBER 17, 2020

Predictive models fit to noise approach 100% accuracy. For example, it’s impossible to know if your predictive model is accurate because it is fitting important variables or noise. As we are still left with a Large P Small N problem, direct hypothesis testing is not possible. The 12 are listed in Table 1.

Statistics 131

Statistics Measurement Testing Predictive Modeling 131

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 145

Visualization Slice and Dice Predictive Modeling Metrics 145

datapine

JANUARY 31, 2022

Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated. This methodology of “test, look at the data, adjust” is at the heart and soul of business intelligence. Your Chance: Want to try a professional BI analytics software?

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

CIO Business Intelligence

JUNE 7, 2022

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

DataRobot

JANUARY 20, 2022

Analysts, data scientists, and citizen data champions can access data and advanced insights on-demand from all ICS partner organizations and collaborate on dataset and model development in real-time. As ICSs mature digitally, there is a need to ensure that all processes, datasets, and models are transparent and are free from bias. –

Data-driven 145

Data-driven Experimentation Predictive Modeling Data Warehouse 145

DataKitchen

JUNE 19, 2023

Our customers start looking at the data in dashboards and models and then find many issues. Using automated data validation tests, you can ensure that the data stored within your systems is accurate, complete, consistent, and relevant to the problem at hand. The image above shows an example ‘’data at rest’ test result.

Testing 100

Testing Data Quality Metrics Cost-Benefit 100

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

Smarten

SEPTEMBER 30, 2024

Knowledgebase Articles Datasets & Cubes : Spark Sql : Split the dataset columns using Regex Dashboards Improve dashboard loading performance for dashboards with multiple objects Integration API : API Call to rebuild cubes / datasets Predictive Use cases Assisted predictive modelling : Classification : Machine Maintenance Using Smarten Assisted (..)

Predictive Modeling 69

Predictive Modeling Dashboards Sales Visualization 69

Smart Data Collective

SEPTEMBER 17, 2020

Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. Big Data can efficiently enhance the ways firms utilize predictive models in the risk management discipline. The Role of Big Data.

Big Data 145

Big Data Finance Risk Management Risk 145

Smarten

JULY 30, 2024

Assisted Predictive Modeling Enables Business Users to Predict Results with Easy-to-Use Tools! Gartner predicted that, ‘75% of organizations will have deployed multiple data hubs to drive mission-critical data and analytics sharing and governance.’

Predictive Analytics 64

Predictive Analytics Prescriptive Analytics Predictive Modeling Analytics 64

Smarten

MAY 9, 2022

Predictive Use cases. Independent Sample T-test Using Smarten Augmented Analytics. Customer Churn model using Smarten Assisted Predictive Modelling. LDAP/AD : AD Integration in Smarten. Embedded / API Integration : API Call to rebuild cubes / datasets. Forum Topics. LDAP/AD : How to configure AD in Smarten?

Predictive Modeling 78

Predictive Modeling Dashboards Testing Modeling 78

Smarten

MAY 13, 2024

Incorporate PMML Integration Within Augmented Analytics to Easily Manage Predictive Models! PMML is Predictive Model Markup Language. It is an interchange format that provides a method by which analytical applications and software can describe and exchange predictive models. So, what is PMML Integration?

Predictive Modeling 64

Predictive Modeling Data mining Predictive Analytics Modeling 64

DataRobot

OCTOBER 19, 2021

DataRobot helped combat this problem head on by applying AI to evaluate and predict resource allocation and identify the most impacted communities from a national to county level. On average, DataRobot forecasts had a 21 percent lower rate of error than all other published competing models over a six to eight week period.

Forecasting 98

Forecasting Predictive Modeling Optimization Reporting 98

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 143

IT Data Processing Enterprise Modeling 143

CIO Business Intelligence

JULY 24, 2023

It has also developed predictive models to detect trends, make predictions, and simulate results. AI takes that data and combines it with historical tracking data from about 2,000 matches to create new insights, such as the Goal Probability model, one of 21 new stats it debuted in 2022.

Broadcasting 98

Broadcasting Recreation/Entertainment Testing Machine Learning 98

CIO Business Intelligence

DECEMBER 11, 2023

This can include steps like replacing the traditional net present value/discounted cash flow calculator with multi-scenario models to stress-test multiple different forecasts under countless different scenarios.

Finance 105

Finance Forecasting IoT Predictive Modeling 105

Smarten

FEBRUARY 1, 2024

Knowledgebase Articles Datasets & Cubes : Calculating Pending Completion Months for an Ongoing Project General : Publish : Working with E-mail Delivery and Publishing Task Installation : Installation on Windows : Bypassing Smarten executable files from Antivirus Scan Predictive Use cases Assisted predictive modelling : Classification : Customer (..)

Predictive Modeling 64

Predictive Modeling Publishing Testing Modeling 64