O'Reilly on Data

OCTOBER 19, 2021

Not only is data larger, but models—deep learning models in particular—are much larger than before. The applications must be integrated to the surrounding business systems so ideas can be tested and validated in the real world in a controlled manner. An Overarching Concern: Correctness and Testing. Who did what and when?

IT 342

IT Testing Experimentation Software 342

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

O'Reilly on Data

MARCH 7, 2019

Language understanding benefits from every part of the fast-improving ABC of software: AI (freely available deep learning libraries like PyText and language models like BERT ), big data (Hadoop, Spark, and Spark NLP ), and cloud (GPU's on demand and NLP-as-a-service from all the major cloud providers). IBM Watson NLU.

Deep Learning 224

Deep Learning Testing Machine Learning Modeling 224

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 96

Data Warehouse Cost-Benefit Dashboards Optimization 96

IBM Big Data Hub

NOVEMBER 29, 2023

Creating synthetic test data to expedite testing, optimization and validation of new applications and features. Here are two common metrics that, while not comprehensive, serve as a solid foundation: Leakage score : This score measures the fraction of rows in the synthetic dataset that are identical to the original dataset.

Metrics 81

Metrics Machine Learning Risk Statistics 81

O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Because ML models can react in very surprising ways to data they’ve never seen before, it’s safest to test all of your ML models with sensitivity analysis. [9] Residual analysis.

Machine Learning 187

Machine Learning Modeling Testing Risk Management 187

CIO Business Intelligence

JUNE 7, 2022

Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning. Prescriptive analytics is a type of advanced analytics that involves the application of testing and other techniques to recommend specific solutions that will deliver desired outcomes.

Data Analytics 122

Data Analytics Diagnostic Analytics Management Analytics 122

Smart Data Collective

FEBRUARY 20, 2022

With these tools, your SaaS can: Merge and improve the application code constantly Automate the development, testing, and release of software Integrate operations and developer workflows And much more. AWS also offers developers the technology to develop smart apps using machine learning and complex algorithms.

Cost-Benefit 114

Cost-Benefit Data Lake Software Machine Learning 114

DataRobot Blog

MARCH 9, 2022

With that said, recent advances in deep learning methods have allowed models to improve to a point that is quickly approaching human precision on this difficult task. The IMDB Movie Reviews Dataset provides 50,000 highly polarized movie reviews with a 50-50 train/test split. Sentiment analysis datasets. It provides 1.6

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

Decision Management Solutions

FEBRUARY 8, 2021

in partnership with IBM and other organizations is in the process of conducting performance tests on the world’s first autonomous ship and is due to set sail for its first transatlantic voyage this spring. I think you’ll see the parallels with Decision Management as the story unfolds. Marine AI—based in Plymouth, U.K.—in

Machine Learning 93

Machine Learning Testing Deep Learning Optimization 93

CIO Business Intelligence

APRIL 27, 2022

Being crystal clear about “why” will provide the vision for the use case you can seek to develop; it will force you to derive a compelling value proposition and identify the KPIs that will guide and measure success. Digitally resilient business models are becoming mainstream concepts that thrive despite global disruptions.

Strategy 129

Strategy Data-driven Internet of Things Measurement 129

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

IBM Big Data Hub

AUGUST 11, 2023

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. These insights can help drive decisions in business, and advance the design and testing of applications.

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

CIO Business Intelligence

MARCH 30, 2022

For example, transactions that are unusually small might be a sign of fraud, with criminals testing out a card number or account to be sure that it works. For this, the company has turned to gradient machine learning. “We The company next plans to explore new technologies, such as deep learning, Baumhof says.

Machine Learning 142

Machine Learning Testing Risk Data-driven 142

CIO Business Intelligence

JUNE 22, 2023

It’s already being used to help improve operational processes, strengthen customer service, measure employee experience, and bolster cybersecurity efforts, among other applications. AI is quickly becoming an essential part of daily work.

IT 100

IT Machine Learning Deep Learning Technology 100

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? In the future, more advanced AI can make far more detailed risk profiles taking into account biometrics, past claims data, and even lab testing. Are we close to AI reliance?

Insurance 134

Insurance Cost-Benefit Big Data Unstructured Data 134

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

Risk 52

Risk Modeling Metrics Business Objectives 52

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. Step 4: Test the quality of data The success of an AI marketing tool depends on the accuracy and relevancy of the data it’s trained on.

Marketing 79

Marketing Technology Advertising ROI 79

Domino Data Lab

OCTOBER 7, 2020

The Curse of Dimensionality , or Large P, Small N, ((P >> N)) , problem applies to the latter case of lots of variables measured on a relatively few number of samples. MANOVA, for example, can test if the heights and weights in boys and girls is different. P >> N) ). <= 0.001)', 'Pr(Max. >=

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

CIO Business Intelligence

MARCH 22, 2023

The new class often uses advanced techniques such as deep learning, natural language processing, and computer vision to analyze and extract insights from the data. Additionally, federated learning does not address the inference stage, which still exposes data to the ML model during cloud or edge device deployment.

Machine Learning 94

Machine Learning Cost-Benefit Modeling Risk 94

Insight

OCTOBER 25, 2019

This category was not considered for the purpose of this project as it does not allow for a 3-way partition for disjoint training, validation, and testing sets. My client also specified that CAD model files of the T-LESS dataset be used for this project, and that one object per class be reserved for testing (Objects 4, 8, 12, 18, 23, 30).

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. VoxCeleb1 dev test # of speakers 1,211 40 # of videos 21,819 677 # of utterances 148,642 4,874.

Modeling 64

Modeling Testing Machine Learning Measurement 64

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. Be sure test cases represent the diversity of app users. What are the business consequences?

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

IBM Big Data Hub

APRIL 18, 2024

Beyond cost savings, organizations seek tangible ways to measure gen AI’s return on investment (ROI), focusing on factors like revenue generation, cost savings, efficiency gains and accuracy improvements, depending on the use case. This personalized approach might lead to more effective therapies with fewer side effects.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. Prioritizing tracking metrics accurately measures the success of your implementation. Thoroughly testing the AI system before full deployment is critical.

Enterprise 96

Enterprise Interactive Data-driven Key Performance Indicator 96

Data Science and Beyond

OCTOBER 15, 2017

The hardest parts of data science are problem definition and solution measurement, not model fitting and data cleaning , because counting things is hard. I’ve also dabbled in deep learning , marine surveys , causality , and other things that I haven’t had the chance to write about. Should I learn Python or R?

Data Science 71

Data Science Deep Learning Machine Learning Data-driven 71

Cloudera

FEBRUARY 16, 2022

blueberry spacing) is a measure of the model’s interpretability. They define each stage from data ingest, feature engineering, model building, testing, deployment and validation. Figure 04: Applied Machine Learning Prototypes (AMPs). Machine Learning Model Reproducibility . Model Visibility.

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

O'Reilly on Data

MARCH 31, 2020

This has serious implications for software testing, versioning, deployment, and other core development processes. Measurement, tracking, and logging is less of a priority in enterprise software. At measurement-obsessed companies, every part of their product experience is quantified and adjusted to optimize user experience.

Management 327

Management Machine Learning Experimentation Metrics 327

Alation

APRIL 14, 2022

Automatic sampling to test transformation. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. AI/ML models pose a problem with versioning results and testing using unique testing and algorithms. A useful feature for exposing patterns in the data.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

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

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

Domino Data Lab

JUNE 23, 2019

Be aware that machine learning often involves working on something that isn’t guaranteed to work. As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. Testing is critical. It is similar to R&D.

Management 110

Management Machine Learning Experimentation Metrics 110

Edwin Chen

OCTOBER 6, 2014

How do you measure its quality, to make sure that it's sending users relevant and personalized content? Is CTR a good measure? So on many online sites, it's unclear how to measure the quality of personalization and recommendations using metrics like CTR, or revenue, or dwell time, or whatever. Take Google's search engine.

Metrics 79

Metrics Measurement Machine Learning Testing 79

Occam's Razor

OCTOBER 12, 2017

When you go to the interview, the hiring company will proceed to ask questions that test your competency in the listed job requirements. Test for analytics experience AND explore the level of analytical thinking the job candidate possesses. Intro to Machine Learning. Machine Learning. Deep Learning.

Marketing 136

Marketing Analytics Machine Learning Strategy 136

Domino Data Lab

APRIL 3, 2019

Network security mushrooms with VPNs, IDS , gateways, various bump-in-the-wire solutions, SIMS tying all the anti-intrusion measures within the perimeter together, and so on. We find ways to improve machine learning so that it requires orders of magnitude more data, e.g., deep learning with neural networks. credit cards).

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84