CIO Business Intelligence

MARCH 7, 2023

Vince Kellen understands the well-documented limitations of ChatGPT, DALL-E and other generative AI technologies — that answers may not be truthful, generated images may lack compositional integrity, and outputs may be biased — but he’s moving ahead anyway. And, he says, using generative AI for coding has worked well.

Enterprise 112

Enterprise IT Experimentation Unstructured Data 112

IBM Big Data Hub

OCTOBER 4, 2022

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation. Multiple unsupported tools for building and deploying models. Platforms and practices not optimized for AI. This includes capturing of the metadata, tracking provenance and documenting the model lifecycle.

Metadata 102

Metadata Risk Risk Management Experimentation 102

CIO Business Intelligence

OCTOBER 18, 2023

Of roughly 2,500 CIOs surveyed recently by Gartner, 9% say they have already deployed gen AI applications, and a staggering 55% say they will deploy large language models (LLMs) in production by the end of 2025. in concert with Microsoft’s AI-optimized Azure platform. John Spottiswood, COO of Jerry, a Palo Alto, Calif.-based

Risk 132

Risk Manufacturing Enterprise Technology 132

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

CIO Business Intelligence

JUNE 28, 2023

Experimentation with a use case driven approach. For those companies that have chosen to experiment, they’re doing so largely in a controlled sandbox environment, with an emphasis on learning and understanding how to pair the technology with human capabilities to optimize for value and risk. Looking forward.

Enterprise 52

Enterprise Experimentation Risk Data-driven 52

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. Data science teams of all sizes need a productive, collaborative method for rapid AI experimentation. Capabilities Beyond Classic Jupyter for End-to-end Experimentation. Auto-scale compute.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Ontotext

NOVEMBER 2, 2023

The data itself is stored in a way that is not optimal for extracting insight. However, it will take effort to formalize a shared semantic model that can be mapped to data assets, and turn unstructured data into a format that can be mined for insight. This is partly because integrating and moving data is not the only problem.

IT 69

IT Cost-Benefit Data-driven Technology 69

CIO Business Intelligence

MAY 1, 2024

Gen AI takes us from single-use models of machine learning (ML) to AI tools that promise to be a platform with uses in many areas, but you still need to validate they’re appropriate for the problems you want solved, and that your users know how to use gen AI effectively. Pilots can offer value beyond just experimentation, of course.

Cost-Benefit 138

Cost-Benefit Metrics Insurance Measurement 138

CIO Business Intelligence

MAY 9, 2024

Experimentation drives momentum: How do we maximize the value of a given technology? Via experimentation. This can be as simple as a Google Sheet or sharing examples at weekly all-hands meetings Many enterprises do “blameless postmortems” to encourage experimentation without fear of making mistakes and reprisal.

Enterprise 97

Enterprise Experimentation Sales Optimization 97

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Let's listen in as Alistair discusses the lean analytics model… The Lean Analytics Cycle is a simple, four-step process that shows you how to improve a part of your business. But it is not routine.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

CIO Business Intelligence

APRIL 24, 2024

But to find ways it can help grow a company’s bottom line, CIOs have to do more to understand a company’s business model and identify opportunities where gen AI can change the playing field. We’re mostly still optimizing our sales and marketing processes with CRM tools,” he says. And hyper-personalization at scale.”

Sales 123

Sales Finance Experimentation Cost-Benefit 123

DataRobot Blog

SEPTEMBER 30, 2022

How do you track the integrity of a machine learning model in production? Model Observability can help. By tracking service, drift, prediction data, training data, and custom metrics, you can keep your models and predictions relevant in a fast-changing world. Model Observability Features.

Slice and Dice 52

Slice and Dice Modeling Metrics Experimentation 52

O'Reilly on Data

MARCH 23, 2023

Many of these go slightly (but not very far) beyond your initial expectations: you can ask it to generate a list of terms for search engine optimization, you can ask it to generate a reading list on topics that you’re interested in. It’s important to understand that ChatGPT is not actually a language model. with specialized training.

IT 262

IT Modeling Testing Risk 262

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. It should be noted that inverse probability weighting is not generally optimal (i.e., This is often referred to as the positivity assumption. the curse of dimensionality).

Statistics 44

Statistics Optimization Modeling Experimentation 44

Occam's Razor

AUGUST 22, 2011

Key To Your Digital Success: Web Analytics Measurement Model. Web Data Quality: A 6 Step Process To Evolve Your Mental Model. Convert Data Skeptics: Document, Educate & Pick Your Poison. Build A Great Web Experimentation & Testing Program. Experimentation and Testing: A Primer. What's The Fix?

KPI 124

KPI Metrics Measurement ROI 124

IBM Big Data Hub

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platforms assist with a multitude of tasks ranging from enforcing data governance to better workload distribution to the accelerated construction of machine learning models.

Machine Learning 78

Machine Learning Manufacturing Cost-Benefit Deep Learning 78

Occam's Razor

DECEMBER 9, 2009

some reports going out on schedule (even if data pukes) then an optimal path might be to centralize some where (see item #2 below). [B] Which physical organizational model will work best for you? My organization redesign plans have recommended either one of the three models. Ok, only if it's legal where you live. ;)].

Analytics 95

Analytics Data-driven Marketing Sales 95

CIO Business Intelligence

APRIL 5, 2023

Midjourney, ChatGPT, Bing AI Chat, and other AI tools that make generative AI accessible have unleashed a flood of ideas, experimentation and creativity. That turns generic documentation into conversational programming where the AI can take your data and show you how to write a query, for example.

Modeling 94

Modeling Testing Cost-Benefit Experimentation 94

CIO Business Intelligence

MARCH 5, 2024

Many other platforms, such as Coveo’s Relative Generative Answering , Quickbase AI , and LaunchDarkly’s Product Experimentation , have embedded virtual assistant capabilities but don’t brand them copilots. Today, top AI-assistant capabilities delivering results include generating code, test cases, and documentation.

Cost-Benefit 112

Cost-Benefit Advertising Experimentation Software 112

Rocket-Powered Data Science

OCTOBER 16, 2020

We build models to test our understanding, but these models are not “one and done.” In ML, the learning cycle is sometimes called backpropagation, where the errors (inaccurate predictions) of our models are fed back into adjusting the model’s input parameters in a way that aims to improve the output accuracy. That’s data.

Digital Transformation 142

Digital Transformation Experimentation Data Strategy Strategy 142

O'Reilly on Data

JULY 25, 2023

I’m a professor who is interested in how we can use LLMs (Large Language Models) to teach programming. Here’s how I worked on it: I subscribed to ChatGPT Plus and used the GPT-4 model in ChatGPT (first the May 12, 2023 version, then the May 24 version) to help me with design and implementation. I saw an example for Node.js

Consulting 246

Consulting Interactive Software IT 246

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

Corinium

APRIL 25, 2019

All assets need to be optimally leveraged for maximum business value while also being protected from misuse, whether there was malicious intent or not, and this needs to be the responsibility of whomever is responsible for that asset in the company. This is a much more proactive and scalable model.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. SQL optimization provides helpful analogies, given how SQL queries get translated into query graphs internally , then the real smarts of a SQL engine work over that graph. Introduction. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Domino Data Lab

FEBRUARY 12, 2020

for model serving (experimental), are implemented with Ray internally for its scalable, distributed computing and state management benefits, while providing a domain-specific API for the purposes they serve. Motivations for Ray: Training a Reinforcement Learning (RL) Model. Handling Stateful Computation with an Actor Model.

Experimentation 137

Experimentation Modeling Data Science Management 137

AWS Big Data

JULY 26, 2023

The vector engine supports the popular distance metrics such as Euclidean, cosine similarity, and dot product, and can accommodate 16,000 dimensions, making it well-suited to support a wide range of foundational and other AI/ML models. After the collection is successfully created, you can create the vector index.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

Cloudera

NOVEMBER 25, 2020

For example, it includes patient samples (blood, blood pressure, temperature, and more), patient information (age, gender, where they have lived, family situation, and other details), and treatment history, most of which is currently found only in paper documents. Innovate on serviceability and optimize utilization.

Data Warehouse 57

Data Warehouse Unstructured Data Analytics Visualization 57

Domino Data Lab

AUGUST 26, 2019

Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Data scientists and researchers ascertain predictive accuracy of models using different techniques, methodologies, and settings, including model parameters and hyperparameters.

Testing 79

Testing Modeling Machine Learning Metrics 79

O'Reilly on Data

SEPTEMBER 11, 2019

So, we used a form of the Term Frequency-Inverse Document Frequency (TF/IDF) technique to identify and rank the top terms in this year’s Strata NY proposal topics—as well as those for 2018, 2017, and 2016. 2 in frequency in proposal topics; a related term, “models,” is No. An ML-related topic, “models,” was No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

CIO Business Intelligence

AUGUST 1, 2023

The impact of generative AIs, including ChatGPT and other large language models (LLMs), will be a significant transformation driver heading into 2024. Mitigate risks by communicating an LLM governance model The generative AI landscape has more than 100 tools covering test, image, video, code, speech, and other categories.

Digital Transformation 97

Digital Transformation Unstructured Data Strategy Experimentation 97

O'Reilly on Data

MAY 11, 2020

Not all models are created equal, however: they operate on different principles, and impact us as individuals and communities in different ways. To understand the menagerie of models that are fundamentally altering our individual and shared realities, we need to build a typology, a classification of their effects and impacts.

Modeling 190

Modeling Advertising Recreation/Entertainment Metrics 190