O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). A lot to learn, but worthwhile to access the unique and special value AI can create in the product space.

Management 332

Management Machine Learning Experimentation Metrics 332

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 88

Metrics Machine Learning Risk Statistics 88

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI As model building become easier, the problem of high-quality data becomes more evident than ever. In this post, we shed some light on various efforts toward generating data for machine learning (ML) models.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

O'Reilly on Data

FEBRUARY 11, 2019

Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning. In a forthcoming survey, “Evolving Data Infrastructure,” we found strong interest in machine learning (ML) among respondents across geographic regions.

Big Data 209

Big Data Technology Machine Learning Deep Learning 209

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Fair warning: if the business lacks metrics, it probably also lacks discipline about data infrastructure, collection, governance, and much more.) Identifying the problem. Agreeing on metrics.

Marketing 362

Marketing Experimentation Metrics Testing 362

DataRobot

JULY 16, 2021

The first is trust in the performance of your AI/machine learning model. They all serve to answer the question, “How well can my model make predictions based on data?” How can identifying gaps or discrepancies in the training data help you build a more trustworthy model? How large is the data set? Operations.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. And last is the probabilistic nature of statistics and machine learning (ML).

Risk 359

Risk Modeling Data Processing Software 359

Corinium

JUNE 6, 2019

Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments. Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

IBM Big Data Hub

FEBRUARY 23, 2024

NLP translates the user’s words into machine actions, enabling machines to understand and respond to customer inquiries accurately. Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. Today, people don’t just prefer instant communication; they expect it.

Enterprise 105

Enterprise Interactive Data-driven Key Performance Indicator 105

Cloudera

DECEMBER 3, 2021

There is no moral compass, no frame of reference of what is fair unless we define one. This involves identifying, quantifying and being able to measure ethical considerations while balancing these with performance objectives. For example, training an interview screening model using education data often contains gender information.

Uncertainty 98

Uncertainty Measurement Metrics Risk 98

Smart Data Collective

JUNE 3, 2021

Many different industries are becoming more reliant on machine learning. The insurance industry is among those that has found new opportunities to take advantage of machine learning technology. Many of the applications of big data for insurance companies will be realized with machine learning technology.

Insurance 98

Insurance Machine Learning Cost-Benefit Risk 98

Rita Sallam

JUNE 2, 2023

The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms. For the vendors that participate in the Bake-Off and Show Floor Showdowns, it is in equal measure fun and extremely stressful. What Data Did We Use?

Optimization 52

Optimization Insurance Machine Learning Risk 52

DataRobot

AUGUST 9, 2021

However, many organizations don’t implement any feedback loop to monitor and control their models after they’re out in production. General Data Protection Regulation, California Consumer Privacy Act, SR 11-7 Guidance on Model Risk Management, etc). DataRobot MLOps solution is ideal in this journey of fairness and trustworthy AI.

Machine Learning 52

Machine Learning Risk Testing Insurance 52

IBM Big Data Hub

SEPTEMBER 27, 2022

We believe that data policies should be fair and equitable and prioritize openness. Is it fair? These questions translate into five fundamental principles for trustworthy AI: fairness, robustness, privacy, explainability and transparency. But with greater AI adoption comes greater challenges. The principles of AI ethics.

Consulting 52

Consulting Machine Learning Modeling Strategy 52

CIO Business Intelligence

MAY 26, 2022

Expectedly, advances in artificial intelligence (AI), machine learning (ML), and predictive modeling are giving enterprises – as well as small/medium-sized businesses – a never-before opportunity to automate their recruitment even as they deal with radical changes in workplace practices involving remote and hybrid work.

Advertising 116

Advertising Machine Learning Software Measurement 116

DataRobot

APRIL 20, 2021

Unlike an intrinsic quality such as accuracy or even fairness, trust is a characteristic of the human-machine relationship. In this installment, I’ll cover four key elements of trusted AI that relate to the performance of a model: data quality, accuracy, robustness and stability, and speed. Use Multiple Tools and Visualizations.

Measurement 52

Measurement Visualization Advertising Metrics 52

IBM Big Data Hub

FEBRUARY 7, 2024

This may include using more eco-friendly packaging or production materials and reducing waste, as well as human-centered efforts such as ensuring fair labor practices and safe work environments. In 2024, sustainability is taking center stage. Businesses, governments and individuals now see sustainability as a global imperative.

Reporting 98

Reporting Cost-Benefit Internet of Things Data-driven 98

CIO Business Intelligence

MAY 28, 2022

Expectedly, advances in artificial intelligence (AI), machine learning (ML), and predictive modeling are giving enterprises – as well as small/medium-sized businesses – a never-before opportunity to automate their recruitment even as they deal with radical changes in workplace practices involving remote and hybrid work.

Advertising 109

Advertising Machine Learning Software Measurement 109

Smart Data Collective

JUNE 4, 2021

Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Probability is the measurement of the likelihood of events. Programming knowledge is needed for the typical tasks of transforming data, creating graphs, and creating data models. Basics of Machine Learning.

Data Science 105

Data Science Statistics Deep Learning Machine Learning 105

Rita Sallam

APRIL 2, 2023

The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms. For the vendors that participate in the Bake-Off and Show Floor Showdowns, it is in equal measure fun and extremely stressful. We did two Bake-Offs this year.

Optimization 52

Optimization Machine Learning Insurance Risk 52

Cloudera

NOVEMBER 15, 2021

In other words, structured data has a pre-defined data model , whereas unstructured data doesn’t. . The word “data” is ubiquitous in narratives of the modern world. And data, the thing itself, is vital to the functioning of that world. This blog discusses quantifications, types, and implications of data. Quantifications of data.

Unstructured Data 86

Unstructured Data Recreation/Entertainment Structured Data Reporting 86

Domino Data Lab

NOVEMBER 14, 2019

James Murdoch, Chandan Singh, Karl Kumber, and Reza Abbasi-Asi’s recent paper, “Definitions, methods, and applications in interpretable machine learning” Introduction. Model interpretability continues to spark public discourse among industry. Yet, Bin Yu, W. ” and advocate. ” Yu et al.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

datapine

JANUARY 6, 2022

Yet, before any serious data interpretation inquiry can begin, it should be understood that visual presentations of data findings are irrelevant unless a sound decision is made regarding scales of measurement. good, very good, fair, etc., For a more in-depth review of scales of measurement, read our article on data analysis questions.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

Cloudera

MAY 27, 2021

It’s important to be conscious of this reality when creating algorithms and training models. Turning a blind eye to problems or applying half measures isn’t going to work. Sometimes, even if everything is done to deliver ethical outcomes, the machine may still make predictions and assumptions that don’t abide by these rules.

Machine Learning 115

Machine Learning Big Data Consulting Technology 115

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. In 2013, Robert Galbraith?—?an an aspiring author?—?finished finished his first novel, Cuckoo’s Calling. often without even looking at it.

Modeling 59

Modeling Metadata Publishing Sales 59

Business Over Broadway

MARCH 13, 2019

IBM Watson Studio , an end-to-end analytics solution to help you gain insights from your data, was designed for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. Why IBM Watson Studio? Communications from the Company. Purchasing Loyalty (e.g,

Statistics 78

Statistics Measurement Descriptive Analytics Machine Learning 78

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. A complementary Domino project is available. . Introduction. Chapter Introduction: Causal Inference.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

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.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

Domino Data Lab

APRIL 3, 2019

Plus, the more mature machine learning (ML) practices place greater emphasis on these kinds of solutions than the less experienced organizations. That presented an opportunity to learn, putting me in the same position as much of the audience. Rinse, lather, repeat. Those are table stakes in this game.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”. The field of AI product management continues to gain momentum.

Management 244

Management Metrics Machine Learning Modeling 244

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 covered different ways of measuring model efficiency and showed ways to visualize this and select models based on it.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

DataRobot Blog

JULY 21, 2022

Download the Machine Learning Project Checklist. Planning Machine Learning Projects. Machine learning and AI empower organizations to analyze data, discover insights, and drive decision making from troves of data. More organizations are investing in machine learning than ever before.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

Cloudera

SEPTEMBER 7, 2023

With the emergence of new creative AI algorithms like large language models (LLM) fromOpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa, and Bloomberg’s BloombergGPT—awareness, interest and adoption of AI use cases across industries is at an all time high. But these measures alone may not be sufficient to protect proprietary information.

Insurance 79

Insurance Risk Data-driven Finance 79

O'Reilly on Data

OCTOBER 11, 2022

And it’s never bubbled up far enough into our consciousness to make it into our monthly Trends piece. As a trend, we’re openly skeptical about Hyperautomation. However, that skeptical conclusion is too simplistic. Hyperautomation may just be another ploy in the game of buzzword bingo, but we need to look behind the game to discover what’s important.

Data Integration 291

Data Integration Insurance Dashboards Modeling 291

IBM Big Data Hub

FEBRUARY 22, 2024

Earning trust in the outputs of AI models is a sociotechnical challenge that requires a sociotechnical solution. But it’s equally important that they have a deep understanding of the risks and limitations of AI and how to implement the appropriate security measures and ethics guardrails.

Metadata 101

Metadata Measurement Risk Modeling 101

O'Reilly on Data

MARCH 23, 2023

It’s important to understand that ChatGPT is not actually a language model. It’s a convenient user interface built around one specific language model, GPT-3.5, is one of a class of language models that are sometimes called “large language models” (LLMs)—though that term isn’t very helpful. It has helped to write a book.

IT 262

IT Modeling Testing Risk 262

CIO Business Intelligence

AUGUST 15, 2023

Shamim Mohammad, CIO, CarMax CarMax That volume created a Sisyphean task for the company’s content writers, as they struggled to provide up-to-date information by make, model, and year for each vehicle in the company’s constantly changing inventory. They turned to artificial intelligence to help.

IT 91

IT Manufacturing IoT Cost-Benefit 91