DataKitchen

OCTOBER 28, 2021

ML models are being used to aid disease diagnosis like detecting cancer cells, tag photographs in social media, understand speech, identify credit card fraud, increase customer engagement with movie/TV recommendations on video streaming services and much more. Addressing AI Bias With DataOps. About the Author. Chris Bergh.

Testing 130

Testing IT Manufacturing Metrics 130

datapine

JUNE 7, 2021

As the most powerful search engine that exists today, Google’s search algorithm forms the basis for SEO efforts undertaken by companies. Google algorithms are updated now and then, but the goal that every website chases remains the same – consistent increase in domain authority. Keyword Rankings. KPIs for Authority.

Metrics 205

Metrics Optimization KPI Visualization 205

IBM Big Data Hub

DECEMBER 18, 2023

Synthetic data is where algorithms and simulations attempt to create the experience of an “average” user based on representative data samples. NS1 Connect adds JavaScript tags to that web property which collect information about inbound user traffic. Are there alternatives to RUM data? Why “real”? Actually, yes!

IT 68

IT Metrics Optimization Testing 68

Octopai

MARCH 31, 2021

If the connection between two datasets was made by a data processing algorithm, it may go over your pattern-based data lineage’s head. Data Lineage by Tagging or Self-Contained Data Lineage. This data lineage technique reads and understands the algorithms used to process, transform and transmit the data. Data Lineage by Parsing.

Metadata 130

Metadata Data Transformation Business Intelligence Reporting 130

Ontotext

MAY 26, 2021

One of the main uses of the Gold Standard is to train AI systems to identify the patterns in various types of data with the help of machine learning (ML) algorithms. We can also use the Gold Standard to evaluate the performance of such algorithms. Then this sample set was used for fine-tuning the ML algorithms even further.

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

CIO Business Intelligence

SEPTEMBER 12, 2023

Everyone is still amazed by the way the generative AI algorithms can whip off some amazing artwork in any style and then turn on a dime to write long essays with great grammar. Generative AI algorithms are still very new and evolving rapidly, but it’s still possible to see cracks in the foundation. The stock prices are soaring.

Enterprise 116

Enterprise Consulting Technology IT 116

Smart Data Collective

AUGUST 11, 2022

This massive undertaking requires input from groups of people to help correctly identify objects, including digitization of data, Natural Language Processing, Data Tagging, Video Annotation, and Image Processing. However, Ai uses algorithms that can screen and handle large data sets. Elimination of Human Mistakes.

Data Quality 129

Data Quality Machine Learning Digital Transformation Big Data 129

Smart Data Collective

OCTOBER 13, 2022

AI algorithms can check and validate if a product meets QA in real-time, significantly easing the process. Bias in Data and Algorithms. If the data and algorithms companies use to train AI are inherently biased, that can lead to some big problems. It involves tasks such as inspection, testing, and evaluation.

Testing 113

Testing Cost-Benefit Experimentation Optimization 113

Laminar Security

DECEMBER 13, 2023

Generally, AI algorithms can sift through vast amounts of data at an astonishing speed, identifying patterns and correlations that would be impossible for humans to detect as rapidly and flawlessly. In the context of data discovery, ML algorithms can learn from past data exploration experiences to enhance future ones.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

Smart Data Collective

APRIL 24, 2020

Of course, at this point, data is going to be properly tagged and organized for easy access, but being able to keep everything as is makes data lakes pretty powerful. Uses Powerful Algorithms. A good perk to point out about data lakes is that they can use powerful algorithms to help your analysts comprehend the gathered data.

Data Lake 99

Data Lake Data Collection Deep Learning Management 99

Smart Data Collective

JULY 28, 2021

While some jobs must be performed by actual humans, many can be performed just as well through algorithms, machines, and other technologies. You can achieve this by investing in RFID (radio frequency identification) tags. In effect, the small tags turn red (from green) when they’ve been dropped or heavily jolted.

Data-driven 134

Data-driven Big Data Manufacturing ROI 134

Ontotext

FEBRUARY 1, 2024

When our auto-categorization engine, Graphite Knowledge Studio, tags content with metadata derived from taxonomies, we also capture and store this content metadata inside the knowledge graph, thereby linking concepts to the content they describe. What do we mean by content aware knowledge graphs?

Enterprise 59

Enterprise Management Metadata Modeling 59

IBM Big Data Hub

OCTOBER 16, 2023

ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history. ML classification algorithms are also used to label events as fraud, classify phishing attacks and more.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

Smart Data Collective

FEBRUARY 15, 2022

Artificial intelligence algorithms have been instrumental in fighting plagiarism. These programs use algorithms to send web crawlers across the Internet. As a result, they can index countless web pages that don’t have the “nocrawl” attribute in their meta tags. AI Has Made Plagiarism Detection More Effective than Ever.

Machine Learning 115

Machine Learning Deep Learning Software Technology 115

O'Reilly on Data

JULY 28, 2020

A recent article in The Verge discussed PULSE , an algorithm for “upsampling” digital images. PULSE could be used for applications like upsampling video for 8K ultra high-definition, but I’m less interested in the algorithm and its applications than in the discussion about ethics that it provoked. Who stands to gain?

Testing 135

Testing Statistics Recreation/Entertainment Software 135

Smart Data Collective

MARCH 26, 2021

Due to its constant learning and evolution, the algorithms are able to adapt based on success and failure. Of course, these algorithms aren’t perfect, but they become more refined with every interaction. The algorithms learn based on your behavior and try to recommend products it thinks you will like.

Machine Learning 141

Machine Learning Interactive Deep Learning Technology 141

CIO Business Intelligence

NOVEMBER 7, 2022

Developing models isn’t trivial, and data scientists certainly have challenges cleansing and tagging data, selecting algorithms, configuring models, setting up infrastructure, and validating results. Leaders who want to improve AI delivery performance should address this first question: are data scientists set up for success?

Machine Learning 124

Machine Learning Data Science Experimentation IoT 124

Smart Data Collective

SEPTEMBER 7, 2022

They prioritize on-site SEO actions such as title tags and meta description optimization, website speed, quality content, and streamlining of site navigation. These analytics algorithms help assess the spam scores and authority of different websites. However, it is important to first get an understanding of offsite SEO.

Data mining 134

Data mining Strategy Marketing Optimization 134

Smart Data Collective

APRIL 10, 2020

Algorithmic bots have revolutionized customer facing services. AI algorithms analyze eCommerce products to deliver accurate sales volume forecasts. Algorithmic sales predictions is an AI trend changing the industry. Artificial Intelligence (AI) is changing the way that eCommerce companies do business.

Sales 143

Sales Visualization Marketing Software 143

Smart Data Collective

AUGUST 18, 2021

Accountants are using new software with sophisticated machine learning algorithms to better address the nuances of their situations. You need robust software solutions with complex machine learning and data analytics algorithms to systematize, simplify, and standardize them for more precise, compliant reports. Image source: Trullion.

Data-driven 121

Data-driven Software Big Data Visualization 121

IBM Big Data Hub

AUGUST 28, 2023

As it pertains to social media data, text mining algorithms (and by extension, text analysis) allow businesses to extract, analyze and interpret linguistic data from comments, posts, customer reviews and other text on social media platforms and leverage those data sources to improve products, services and processes.

Data mining 56

Data mining Machine Learning Deep Learning Marketing 56

erwin

APRIL 2, 2021

Sensitive data – at rest or in motion – that exists in various forms across multiple systems must be automatically tagged, its lineage automatically documented, and its flows depicted so that it is easily found, and its usage easily traced across workflows.

Data Governance 106

Data Governance Cost-Benefit Risk Metadata 106

Smart Data Collective

NOVEMBER 21, 2021

SEO-based artificial intelligence systems are capable of the following: Conducting site performance analysis Assisting with keyword research Improving the quality of your material Making appropriate tag recommendations Assisting advertisers in determining the optimal time to post content. Decrease Cart Abandonment.

Strategy 143

Strategy Advertising Machine Learning Marketing 143

Smart Data Collective

JUNE 29, 2022

You will want to use data mining tools to find the alt tags of similar images on the web. Tools like BeautifulSoup and Screaming Frog SEO use data mining algorithms to find the alt tags of different images, which you can use in your own content. Test, Test, Test. Email marketing is all about experimentation.

B2B 106

B2B Data-driven Marketing Strategy 106

Smart Data Collective

OCTOBER 7, 2023

Not only do people share their personal milestones and daily snippets here, but they also check in at various places, tag friends, and announce plans. Predictive Policing: Some cities are experimenting with AI-driven predictive policing, where algorithms analyze historical crime data to forecast where crimes are likely to occur next.

Technology 93

Technology Snapshot Data-driven Machine Learning 93

IBM Big Data Hub

FEBRUARY 14, 2024

Promote cross- and up-selling Recommendation engines use consumer behavior data and AI algorithms to help discover data trends to be used in the development of more effective up-selling and cross-selling strategies, resulting in more useful add-on recommendations for customers during checkout for online retailers.

Cost-Benefit 86

Cost-Benefit Insurance Unstructured Data Machine Learning 86

Smart Data Collective

JUNE 4, 2021

Data scientists use algorithms for creating data models. Whereas in machine learning, the algorithm understands the data and creates the logic. Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. Where to start? Reinforcement.

Data Science 104

Data Science Statistics Deep Learning Machine Learning 104

Smart Data Collective

FEBRUARY 4, 2021

Likewise, it consists of an auto-tag that will repeat all expenses, letting you differentiate between business and personal expenses. The savvy tracking tools allow you to speed tag the expenses, which means you can tag several expenses in a few taps. You can link the software with different banks and online applications.

Data Analytics 141

Data Analytics Analytics Cost-Benefit Big Data 141

Insight

MARCH 12, 2020

Using nothing more than a book’s synopsis, the AIgent can surface similar books, genre tags, and sales proxies. for example genre tags, ratings, and sales. For my purposes, the most interesting data on Goodreads comes in the form of genre/content tags. Likewise, it will help writers identify agents who are a good fit.

Modeling 59

Modeling Metadata Publishing Sales 59

Jet Global

APRIL 2, 2019

And while AI algorithms are certainly poised to make an impact in each of these areas, enterprise businesses need to first invest in building the infrastructure to support them. There’s nothing to analyze, or apply a learning algorithm to—when it comes to any intelligence solution, data is the foundation upon which it must be built.

Enterprise 80

Enterprise OLAP Business Intelligence Internet of Things 80

erwin

OCTOBER 31, 2019

Sensitive data – at rest or in motion – that exists in various forms across multiple systems must be automatically tagged, its lineage automatically documented, and its flows depicted so that it is easily found and its usage across workflows easily traced.

Data Governance 105

Data Governance Metadata Risk Data Lake 105

O'Reilly on Data

MARCH 7, 2019

In practice, off-the-shelf NLP libraries and algorithms built for English will fail miserably on this “different language” in the health care industry. In particular, the algorithms had to deal with headers and footers, lists, enumerations, call-outs, two-column documents, and other formatting.

Deep Learning 227

Deep Learning Testing Machine Learning Modeling 227

AWS Big Data

OCTOBER 25, 2023

The raw data will be stored in Amazon S3 for archival purposes, then the pipeline will detect anomalies in the data in near-real time using the Random Cut Forest (RCF) algorithm and send those data records to OpenSearch Service. The pipeline filters the data, routes the data, and detects anomalies.

Analytics 119

Analytics Data Processing Optimization Metrics 119

Dataiku

MARCH 25, 2024

Exploring M-LLM Algorithms Numerous M-LLMs have been developed to tackle VQA and Image Captioning. This text information includes tags, attributes, captions. In this section, we delve into three different approaches.

Visualization 132

Visualization Modeling Experimentation Testing 132

erwin

APRIL 11, 2019

By adopting a data governance platform that enables you to automatically tag sensitive data and track its lineage , you can ensure nothing falls through the cracks. Providing metadata and value-based analysis – Simplify the discovery and classification of sensitive data based on metadata and data value patterns and algorithms.

Insurance 100

Insurance Data-driven Data Governance Metadata 100

Sisense

APRIL 30, 2020

To do this, we’ll use machine learning (ML) algorithms, which are based on concepts of linear algebra and statistics. This is a vital practice in NLP and makes data more understandable for the algorithms. Begin by removing the URLs and HTML tags. Preprocessing works like this: 1. get_text().

Machine Learning 91

Machine Learning Statistics Modeling IT 91

Cloudera

AUGUST 18, 2021

To prevent that, companies must implement a strategy to make sense of data by first training AI algorithms and then continually refining them as new, relevant information becomes available. It makes more sense to analyze and derive insights from it, and then place it in the data lake — properly tagged for easy access later.

Data Lake 88

Data Lake IoT Internet of Things Data-driven 88