Analytics Vidhya

DECEMBER 2, 2024

As indicated in machine learning and statistical modeling, the assessment of models impacts results significantly. Meet the F-Beta Score, a more unrestrictive measure that let the user weights precision over recall or […] The post What is F-Beta Score?

Machine Learning 208

Machine Learning Statistics Measurement Modeling 208

O'Reilly on Data

JANUARY 21, 2025

The Evolution of Expectations For years, the AI world was driven by scaling laws : the empirical observation that larger models and bigger datasets led to proportionally better performance. This fueled a belief that simply making models bigger would solve deeper issues like accuracy, understanding, and reasoning.

Cost-Benefit 293

Cost-Benefit Testing Interactive Software 293

Analytics Vidhya

MARCH 29, 2025

Imagine an AI that can write poetry, draft legal documents, or summarize complex research papersbut how do we truly measure its effectiveness? As Large Language Models (LLMs) blur the lines between human and machine-generated content, the quest for reliable evaluation metrics has become more critical than ever.

Metrics 199

Metrics Measurement Modeling Analytics 199

Analytics Vidhya

SEPTEMBER 30, 2022

Introduction Evaluation metrics are used to measure the quality of the model. Selecting an appropriate evaluation metric is important because it can impact your selection of a model or decide whether to put your model into production. This article was published as a part of the Data Science Blogathon.

Metrics 397

Metrics Measurement Data Science Publishing 397

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 276

Machine Learning Modeling Testing Risk Management 276

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

CIO Business Intelligence

JANUARY 23, 2024

Regardless of where organizations are in their digital transformation, CIOs must provide their board of directors, executive committees, and employees definitions of successful outcomes and measurable key performance indicators (KPIs). He suggests, “Choose what you measure carefully to achieve the desired results.

Measurement 141

Measurement Digital Transformation KPI Metrics 141

Analytics Vidhya

APRIL 6, 2025

Evaluating language models has always been a challenging task. How do we measure if a model truly understands language, generates coherent text, or produces accurate responses?

Metrics 147

Metrics Measurement Modeling Analytics 147

O'Reilly on Data

NOVEMBER 19, 2024

Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. See the primary sources “ REALM: Retrieval-Augmented Language Model Pre-Training ” by Kelvin Guu, et al., at Facebook—both from 2020.

Unstructured Data 312

Unstructured Data Structured Data Statistics Modeling 312

O'Reilly on Data

OCTOBER 12, 2021

Kevlin Henney and I were riffing on some ideas about GitHub Copilot , the tool for automatically generating code base on GPT-3’s language model, trained on the body of code that’s in GitHub. The model will certainly need to be re-trained from time to time. Thomas Johnson said, “Perhaps what you measure is what you get.

Testing 363

Testing Measurement Consulting Modeling 363

Analytics Vidhya

JANUARY 15, 2024

In a startling revelation, researchers at Anthropic have uncovered a disconcerting aspect of Large Language Models (LLMs) – their capacity to behave deceptively in specific situations, eluding conventional safety measures.

Measurement 315

Measurement Risk Modeling Analytics 315

Analytics Vidhya

APRIL 8, 2025

Large Language Models (LLMs) have become integral to modern AI applications, but evaluating their capabilities remains a challenge. Traditional benchmarks have long been the standard for measuring LLM performance, but with the rapid evolution of AI, many are questioning their continued relevance.

Measurement 135

Measurement Modeling Analytics 135

CIO Business Intelligence

DECEMBER 9, 2024

This is particularly true with enterprise deployments as the capabilities of existing models, coupled with the complexities of many business workflows, led to slower progress than many expected. Foundation models (FMs) by design are trained on a wide range of data scraped and sourced from multiple public sources.

Enterprise 116

Enterprise Cost-Benefit ROI Measurement 116

O'Reilly on Data

MARCH 25, 2025

Throughout this article, well explore real-world examples of LLM application development and then consolidate what weve learned into a set of first principlescovering areas like nondeterminism, evaluation approaches, and iteration cyclesthat can guide your work regardless of which models or frameworks you choose. How will you measure success?

Testing 168

Testing Data-driven Software Measurement 168

Analytics Vidhya

FEBRUARY 20, 2024

In a bid to bolster cybersecurity measures, Google has unveiled Magika, an AI-driven file detection tool aimed at identifying malicious files with unprecedented speed and accuracy.

Deep Learning 289

Deep Learning Measurement Modeling Analytics 289

CIO Business Intelligence

JANUARY 14, 2025

By articulating fitness functions automated tests tied to specific quality attributes like reliability, security or performance teams can visualize and measure system qualities that align with business goals. Documentation and diagrams transform abstract discussions into something tangible.

Enterprise 119

Enterprise Technology Metrics Measurement 119

CIO Business Intelligence

OCTOBER 24, 2024

Take for instance large language models (LLMs) for GenAI. Then there’s reinforcement learning, a type of machine learning model that trains algorithms to make effective cybersecurity decisions. But when it comes to cybersecurity, AI has become a double-edged sword.

Risk 128

Risk Measurement Optimization Machine Learning 128

David Menninger's Analyst Perspectives

JANUARY 15, 2025

ISG Research asserts that by 2027, one-third of enterprises will incorporate comprehensive external measures to enable ML to support AI and predictive analytics and achieve more consistently performative planning models. Few go deeper or gather external data in a way that makes it accessible across an enterprise.

Predictive Modeling 130

Predictive Modeling Forecasting Predictive Analytics Statistics 130

CIO Business Intelligence

NOVEMBER 7, 2024

As digital transformation becomes a critical driver of business success, many organizations still measure CIO performance based on traditional IT values rather than transformative outcomes. This creates a disconnect between the strategic role that CIOs are increasingly expected to play and how their success is measured.

Digital Transformation 128

Digital Transformation Metrics Measurement Key Performance Indicator 128

CIO Business Intelligence

SEPTEMBER 17, 2024

CISOs can only know the performance and maturity of their security program by actively measuring it themselves; after all, to measure is to know. However, CISOs aren’t typically measuring their security program proactively or methodically to understand their current security program. people, processes, and technology).

Measurement 111

Measurement Metrics Risk Dashboards 111

O'Reilly on Data

JUNE 15, 2023

The world changed on November 30, 2022 as surely as it did on August 12, 1908 when the first Model T left the Ford assembly line. If we want prosocial outcomes, we need to design and report on the metrics that explicitly aim for those outcomes and measure the extent to which they have been achieved.

Metrics 359

Metrics Reporting Measurement Finance 359

David Menninger's Analyst Perspectives

NOVEMBER 19, 2024

Additionally, while the tools available at the time enabled data teams to respond to quality issues, they did not provide a way to identify quality thresholds or measure improvement, making it difficult to demonstrate to the business the value of time spent remedying data-quality problems. With

Data Quality 130

Data Quality Dashboards Data-driven Software 130

CIO Business Intelligence

DECEMBER 10, 2024

Set clear, measurable metrics around what you want to improve with generative AI, including the pain points and the opportunities, says Shaown Nandi, director of technology at AWS. In HR, measure time-to-hire and candidate quality to ensure AI-driven recruitment aligns with business goals.

Sales 143

Sales Metrics Data-driven Unstructured Data 143

CIO Business Intelligence

NOVEMBER 18, 2024

As a result, organisations are continually investing in cloud to re-invent existing business models and leapfrog their competitors. What began as a need to navigate complex pricing models to better control costs and gain efficiency has evolved into a focus on demonstrating the value of cloud through Unit Economics.

Measurement 111

Measurement Modeling Forecasting Metrics 111

CIO Business Intelligence

MAY 6, 2025

There is a clear rise in phishing, and the malicious use of artificial intelligence (AI) is bypassing traditional security measures. The successful ones choose zero trust architecture rather than the network-centric, perimeter-based security models that are unequipped to face the threats of the digital era.

Modeling 66

Modeling Cost-Benefit Risk Technology 66

CIO Business Intelligence

DECEMBER 20, 2024

From the discussions, it is clear that today, the critical focus for CISOs, CIOs, CDOs, and CTOs centers on protecting proprietary AI models from attack and protecting proprietary data from being ingested by public AI models. isnt intentionally or accidentally exfiltrated into a public LLM model?

Risk 105

Risk Measurement Interactive Modeling 105

CIO Business Intelligence

NOVEMBER 7, 2024

The government also plans to introduce measures to support businesses, particularly small and medium-sized enterprises (SMEs), in adopting responsible AI management practices through a new self-assessment tool. Meanwhile, the measures could also introduce fresh challenges for businesses, particularly SMEs.

Risk 130

Risk Management Measurement Consulting 130

CIO Business Intelligence

FEBRUARY 4, 2025

Instead of seeing digital as a new paradigm for our business, we over-indexed on digitizing legacy models and processes and modernizing our existing organization. This only fortified traditional models instead of breaking down the walls that separate people and work inside our organizations. And its testing us all over again.

Digital Transformation 144

Digital Transformation Optimization Sales Enterprise 144

Analytics Vidhya

JUNE 6, 2023

China is taking a significant step forward in regulating generative artificial intelligence (Generative AI) services with the release of draft measures by the Cyberspace Administration of China (CAC). These proposed rules aim to manage and regulate the use of Generative AI in the country.

Measurement 306

Measurement Management Analytics Modeling 306

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 363

Management Machine Learning Experimentation Metrics 363

O'Reilly on Data

MAY 14, 2020

Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model. In reality, many candidate models (frequently hundreds or even thousands) are created during the development process. Modelling: The model is often misconstrued as the most important component of an AI product.

Management 364

Management Experimentation B2B Machine Learning 364

CIO Business Intelligence

OCTOBER 9, 2024

Depending on your needs, large language models (LLMs) may not be necessary for your operations, since they are trained on massive amounts of text and are largely for general use. As a result, they may not be the most cost-efficient AI model to adopt, as they can be extremely compute-intensive.

Strategy 143

Strategy Modeling Management Data Lake 143

CIO Business Intelligence

OCTOBER 22, 2024

By 2028, 40% of large enterprises will deploy AI to manipulate and measure employee mood and behaviors, all in the name of profit. “AI CMOs view GenAI as a tool that can launch both new products and business models. AI has the capability to perform sentiment analysis on workplace interactions and communications.

Enterprise 135

Enterprise Risk IoT Cost-Benefit 135

CIO Business Intelligence

APRIL 8, 2025

In many cases, companies should opt for closed, proprietary AI models that arent connected to the internet, ensuring that critical data remains secure within the enterprise. This approach also creates a measurable framework with RTO [recovery time objective] and RPO [recovery point objective] metrics.

Risk Management 131

Risk Management Risk Management Business Objectives 131

CIO Business Intelligence

DECEMBER 20, 2024

Its an offshoot of enterprise architecture that comprises the models, policies, rules, and standards that govern the collection, storage, arrangement, integration, and use of data in organizations. AI and machine learning models. Data modeling takes a more focused view of specific systems or business cases. Curate the data.

Data Architecture 122

Data Architecture Management Consulting Internet of Things 122

CIO Business Intelligence

APRIL 8, 2025

These strategies, such as investing in AI-powered cleansing tools and adopting federated governance models, not only address the current data quality challenges but also pave the way for improved decision-making, operational efficiency and customer satisfaction. When customer records are duplicated or incomplete, personalization fails.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102