O'Reilly on Data

DECEMBER 12, 2019

If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. Residuals are a numeric measurement of model errors, essentially the difference between the model’s prediction and the known true outcome. Residual analysis.

Machine Learning 285

Machine Learning Modeling Testing Risk Management 285

DataKitchen

APRIL 13, 2021

DataOps requires that teams measure their analytic processes in order to see how they are improving over time. A complete DataOps program will have a unified, system-wide view of process metrics using a common data store. Datatron — Automates deployment and monitoring of AI models.

Testing 304

Testing Machine Learning Consulting Data Science 304

DataRobot

FEBRUARY 10, 2021

Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger is a hands-on guide that helps people with little math background understand and use deep learning quickly. Target leakage helped to explain the very low scores of the deep learning models.

Modeling 98

Modeling Deep Learning Machine Learning Metrics 98

CIO Business Intelligence

AUGUST 13, 2024

Unlike siloed or shallow automation efforts, deep automation architects a perspective that integrates customer experiences, value streams, human-machine collaboration, and synergistic technologies to create intelligent, self-adjusting businesses. Implement real-time dashboards to track performance across the organization.

Data-driven 130

Data-driven Metrics Optimization Deep Learning 130

Smart Data Collective

JANUARY 20, 2020

Most of these tools are powered by a specific Deep Learning engine which also assists in conversions, revenue generation, and better traffic generations. Deep Learning technologies are also in place to measure content performance and the existing trends which eventually make sure whether the existing content plan will work or not.

Deep Learning 128

Deep Learning ROI Data-driven Marketing 128

CIO Business Intelligence

AUGUST 15, 2024

A virtual assistant may save employees time when searching for old documents or composing emails, but most organizations have no idea how much time those tasks have taken historically, having never tracked such metrics before, she says. There are a lot of cool AI solutions that are cheaper than generative AI,” Stephenson says. “The

ROI 143

ROI Cost-Benefit Experimentation Deep Learning 143

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. We also talked about the increased interest in green AI, in which we not only measure the quality of a model based on accuracy but also how big and complex it is.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

Smart Data Collective

JULY 1, 2023

The resulting structured data is then used to train a machine learning algorithm. There are a lot of image annotation techniques that can make the process more efficient with deep learning. Consistency and agreement Establish an agreement metric (e.g., Cohen’s Kappa) to measure inter-annotator agreement.

Machine Learning 93

Machine Learning Metrics Uncertainty Deep Learning 93

Smart Data Collective

DECEMBER 21, 2020

Insurers are already using AI to select rates for customers and measure the risk they may pose, but how will it directly be of use in claims processing? At present, insurers use AI to assess individuals’ risk using quite generalized metrics, often based on their age, location, and gender. More accurate policy pricing.

Insurance 142

Insurance Cost-Benefit Big Data Unstructured Data 142

IBM Big Data Hub

NOVEMBER 29, 2023

Furthermore, as modeling techniques become increasingly sophisticated in data science, including deep learning and predictive and generative models, companies and vendors must work diligently to prevent unintentional connections that could leak a person’s identity and expose them to third-party attacks.

Metrics 80

Metrics Machine Learning Statistics Risk 80

CIO Business Intelligence

AUGUST 21, 2024

It wasn’t just a single measurement of particulates,” says Chris Mattmann, NASA JPL’s former chief technology and innovation officer. “It It was many measurements the agents collectively decided was either too many contaminants or not.” They also had extreme measurement sensitivity. Adding smarter AI also adds risk, of course.

Risk 136

Risk Insurance Cost-Benefit Software 136

IBM Big Data Hub

NOVEMBER 14, 2023

Internal data monetization initiatives measure improvement in process design, task guidance and optimization of data used in the organization’s product or service offerings. Realize : Each data service has an agreed unit of value which is transacted and measured.

Strategy 103

Strategy Data-driven Cost-Benefit Measurement 103

AWS Big Data

OCTOBER 26, 2023

Read on to understand why price-performance matters and how Amazon Redshift price-performance is a measure of how much it costs to get a particular level of workload performance, namely performance ROI (return on investment). Amazon Redshift delivers up to 4.9 times lower cost per user and up to 7.9

Data Warehouse 119

Data Warehouse Cost-Benefit Dashboards Optimization 119

Sisense

AUGUST 6, 2020

This is also an important takeaway for teams seeking to implement AI successfully: Start with the key performance indicators (KPIs) you want to measure your AI app’s success with, and see where that dovetails with your expert domain knowledge. An obvious mechanical answer is: use relevance as a metric.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Business Over Broadway

DECEMBER 20, 2018

Looking ahead to 2018, data professionals are most interested in learning deep learning (41%). Second, the calculation of the metric (a difference score) results in an ambiguous score that is difficult to interpret. Third, the NPS is insufficient in measuring the multidimensional nature of customer loyalty.

Machine Learning 74

Machine Learning Data Science Deep Learning Predictive Analytics 74

Domino Data Lab

JUNE 23, 2019

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. That’s another pattern.

Management 110

Management Machine Learning Experimentation Metrics 110

Cloudera

SEPTEMBER 20, 2022

The state of the art in AI systems for artistic tasks almost universally use deep-learning models, which presuppose a significant amount of compute resources both to create them, and once created to continue to use them for producing images. Access — who can use it? Data — where does it come from? Misuse — what could go wrong?

Deep Learning 88

Deep Learning Cost-Benefit Risk Data-driven 88

Insight

MARCH 4, 2020

Coherence Umass, ranging [-14, 14], is measuring within-topic top word similarity and CV, ranging [0, 1], is an improved sliding-window based version of it. Silhouette score, ranging [-1, 1], is measuring within-cluster consistency. For all these metrics, a larger number means the model is doing better.

Visualization 81

Visualization Metrics Modeling Machine Learning 81

IBM Big Data Hub

SEPTEMBER 6, 2023

More accurate measurement of KPIs: Digital campaigns generate more data than humans can keep up with, which can make measuring the success of marketing initiatives difficult. Modern AI marketing solutions help stakeholders ensure that they are getting the most out of their investment in a campaign.

Marketing 78

Marketing Technology Advertising Machine Learning 78

DataRobot Blog

MAY 26, 2022

However, with the widespread adoption of modern ML techniques, including gradient-boosted decision trees (GBDTs) and deep learning algorithms , many traditional validation techniques become difficult or impossible to apply.

Risk 52

Risk Modeling Metrics Business Objectives 52

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. The technology’s ability to adapt and learn from interactions further refines customer support metrics, including response time, accuracy of information provided, customer satisfaction and problem-resolution efficiency.

Enterprise 95

Enterprise Interactive Data-driven Key Performance Indicator 95

Cloudera

FEBRUARY 16, 2022

blueberry spacing) is a measure of the model’s interpretability. Given the complexity of some ML models, especially those based on Deep Learning (DL) Convolutional Neural Networks (CNNs), there are limits to interpretability. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua?

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Insight

MARCH 25, 2020

After reading this, I hope you can learn how to build deep learning models using TensorFlow Keras, productionalize the model as a Streamlit app, and deploy it as a Docker container on Google Cloud Platform (GCP) using Google Kubernetes Engines (GKE). In this project, I was curious to see if deep learning approaches?—?specifically

Deep Learning 53

Deep Learning Metrics Data Processing Interactive 53

DataRobot Blog

FEBRUARY 14, 2022

Because our dataset contains image data, DataRobot used models that contain deep learning based image featurizers. Typically this means finding out how many predictions have been made, how many requests have been made to the deployment, and other performance-related metrics. Activation Maps.

Machine Learning 52

Machine Learning Metrics Sales Modeling 52

IBM Big Data Hub

AUGUST 11, 2023

MLOps is the next evolution of data analysis and deep learning. Simply put, MLOps uses machine learning to make machine learning more efficient. Conduct validation in the error process—This method measures how good the guesswork was by comparing it to known examples when available.

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

CIO Business Intelligence

JANUARY 4, 2023

Modern data analytics spans a range of technologies, from dedicated analytics platforms and databases to deep learning and artificial intelligence (AI). Alternatively, some in the European Union are shifting their priority from trying to run operations faster, to trying to run them with lower power consumption.

Cost-Benefit 59

Cost-Benefit Machine Learning Uncertainty Modeling 59

Insight

OCTOBER 25, 2019

The latter is particularly restricting, as it violates the prerequisite of many deep learning methods for image classification?—?a A schematic illustration of the meta-learning approach to this problem using Prototypical Networks. Model performance is evaluated on aggregated metrics of individual tasks. Meta-Learning v.s.

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

Domino Data Lab

JULY 17, 2021

Further, deep learning methods are built on the foundation of signal processing. The likelihood function measures the goodness of fit of a statistical model to a sample of data for given values of the unknown parameters. The test set is used to evaluate model performance metrics.

Modeling 64

Modeling Testing Machine Learning Measurement 64

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 364

Marketing Experimentation Metrics Testing 364

Insight

MARCH 28, 2019

To measure this sentiment, Derek classified each sentence in a review as belonging to one of five categories: Culture & Values, Work/Life Balance, Senior Management, Compensation & Benefits, Career Opportunities (the same five dimensions Glassdoor asks employees to rate along).

Dashboards 70

Dashboards Data Science Visualization Machine Learning 70

Edwin Chen

OCTOBER 6, 2014

How do you measure its quality, to make sure that it's sending users relevant and personalized content? Click-through rate may be your initial hope…but after a bit of thought, it's not clear that it's the best metric after all. Is CTR a good measure? So why, so often, do we never try to measure the relevance of our models?

Metrics 79

Metrics Measurement Machine Learning Testing 79

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning. See Wei et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Domino Data Lab

AUGUST 29, 2019

This article provides an excerpt “Deep Reinforcement Learning” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The article includes an overview of reinforcement learning theory with focus on the deep Q-learning. metrics simultaneously.

Optimization 111

Optimization Deep Learning Modeling Metrics 111

Domino Data Lab

MAY 8, 2019

Visualizations are vital in data science work, with the caveat that the information that they convey may be 4-5 layers of abstraction away from the actual business process being measured. measure the subjects’ ability to trust the models’ results. Information can get quite distorted after being abstracted that many times.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

O'Reilly on Data

NOVEMBER 26, 2024

Any metric can be gamed (often called Goodhart’s law )—and grades are no exception, gamed both by faculty who need good ratings from students and by students who want good grades from faculty. Inverse Document Frequency (IDF): Measures how common or rare a word is across a larger collection of documents (or job postings).

B2B 278

B2B Consulting Machine Learning Data-driven 278

CIO Business Intelligence

DECEMBER 18, 2024

Continuous learning was one of the key performance metrics we were measured on. Fast forward to 2014, when I joined IBM as an associate partner in their Innovation Practice for Natural Resources, focusing on Cognitive (Watson IBMs version of AI and deep learning models).

Deep Learning 98

Deep Learning Interactive Reporting Digital Transformation 98

CIO Business Intelligence

MAY 6, 2025

Governance challenges I commonly see: No consistent data definitions between departments No agreed-upon owner for key metrics like revenue or customer lifetime value Shadow IT spinning up their datasets and dashboards These gaps create delays, misalignment and risk. These capabilities are no longer theoretical or experimental.

Prescriptive Analytics 78

Prescriptive Analytics Predictive Analytics Descriptive Analytics ROI 78