CIO Business Intelligence

APRIL 2, 2024

Underpinning these initiatives is a slew of technology capabilities and strategies aimed at accelerating delivery cycles, such as establishing product management disciplines, building cloud architectures, developing devops capabilities, and fostering agile cultures. This dip delays when the business can start realizing the value delivered.

Management 119

Management Digital Transformation Testing Experimentation 119

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. We are also beginning to see researchers share sample code written in popular open source libraries, and some even share pre-trained models. Model development. Model governance. Source: Ben Lorica.

Modeling 193

Modeling Machine Learning Testing Metrics 193

O'Reilly on Data

MAY 14, 2020

In our previous article, What You Need to Know About Product Management for AI , we discussed the need for an AI Product Manager. In this article, we shift our focus to the AI Product Manager’s skill set, as it is applied to day to day work in the design, development, and maintenance of AI products. The AI Product Pipeline.

Management 363

Management Experimentation B2B Machine Learning 363

TDAN

JUNE 6, 2023

Business models are increasingly based on software applications. Implementing an optimized test data management program […] The world is in a digital revolution. IT is operating at a faster pace than ever before and has become a vital component of modern business.

Testing 75

Testing Optimization Software Management 75

O'Reilly on Data

OCTOBER 13, 2020

The field of AI product management continues to gain momentum. As the AI product management role advances in maturity, more and more information and advice has become available. One area that has received less attention is the role of an AI product manager after the product is deployed. Debugging AI Products.

Management 240

Management Metrics Machine Learning Modeling 240

CIO Business Intelligence

APRIL 26, 2024

That data is in the process of being unified on a multilayered platform that offers a variety of data services, including data ingestion, data management, data governance, and data security. The multilayered data platform will enable TransUnion’s customers to perform deep analytics and build complex AI models.

Modeling 92

Modeling IT Machine Learning Data Governance 92

DataRobot Blog

JULY 22, 2022

Using AI-based models increases your organization’s revenue, improves operational efficiency, and enhances client relationships. You need to know where your deployed models are, what they do, the data they use, the results they produce, and who relies upon their results. That requires a good model governance framework.

Modeling 105

Modeling Risk Management Testing Risk 105

CIO Business Intelligence

FEBRUARY 2, 2024

Master data management (MDM) is required to ensure the enterprise’s data is consistent, accurate, and controlled. Customer relationship management (CRM) , supply chain management (SCM) , and compliance all have one thing in common: they rely on an integrated, single view of quality data. for 180 days access.

Management 110

Management Data Governance Cost-Benefit Testing 110

CIO Business Intelligence

MAY 20, 2024

General Stanley McChrystal, who once led Joint Special Operations Command, shared this key insight, “The military’s historic struggle with a lack of intelligence has shifted towards a new challenge: managing an overwhelming influx of information. He tested this hypothesis by having some machines at the facility warm up and others not.

Management 97

Management Interactive Measurement Marketing 97

IBM Big Data Hub

MAY 17, 2024

” Given the statistics—82% of surveyed respondents in a 2023 Statista study cited managing cloud spend as a significant challenge—it’s a legitimate concern. Cloud maturity models (or CMMs) are frameworks for evaluating an organization’s cloud adoption readiness on both a macro and individual service level.

Modeling 92

Modeling Cost-Benefit Optimization Digital Transformation 92

IBM Big Data Hub

APRIL 9, 2024

Preparing and annotating data IBM watsonx.data helps organizations put their data to work, curating and preparing data for use in AI models and applications. “For the Masters we use 290 traditional AI models to project where golf balls will land,” says Baughman. ” Watsonx.ai ” Watsonx.ai

Management 81

Management IT Machine Learning Metrics 81

IBM Big Data Hub

APRIL 5, 2024

Part 2 of this four-part series discusses the complex tasks energy utility companies face as they shift to holistic grid asset management to manage through the energy transition. The first post of this series addressed the challenges of the energy transition with holistic grid asset management.

Management 66

Management Risk Optimization Predictive Modeling 66

DataKitchen

MAY 7, 2024

DataKitchen Training And Certification Offerings For Individual contributors with a background in Data Analytics/Science/Engineering Overall Ideas and Principles of DataOps DataOps Cookbook (200 page book over 30,000 readers, free): DataOps Certificatio n (3 hours, online, free, signup online): DataOps Manifesto (over 30,000 signatures) One (..)

Data Quality 161

Data Quality Testing Consulting Metrics 161

Cloudera

JUNE 29, 2023

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices.

Data Architecture 84

Data Architecture Risk Management Risk Management 84

Smart Data Collective

MAY 18, 2022

A high-quality testing platform easily integrates with all the data analytics and optimization solutions that QA teams use in their work and simplifies testing process, collects all reporting and analytics in one place, can significantly improve team productivity, and speeds up the release. This is not entirely true. Data reporting.

Testing 107

Testing Data Analytics Analytics Big Data 107

Domino Data Lab

AUGUST 6, 2021

All models require testing and auditing throughout their deployment and, because models are continually learning, there is always an element of risk that they will drift from their original standards. As such, model governance needs to be applied to each model for as long as it’s being used.

Machine Learning 145

Machine Learning Modeling Testing Risk 145

TDAN

MARCH 16, 2021

The purpose of this article is to provide a model to conduct a self-assessment of your organization’s data environment when preparing to build your Data Governance program. Take the […].

Data Governance 105

Data Governance Testing Modeling Data Quality 105

DataKitchen

APRIL 13, 2021

Testing and Data Observability. Sandbox Creation and Management. DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers.

Testing 300

Testing Machine Learning Consulting Data Quality 300

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 187

Machine Learning Modeling Testing Risk Management 187

AWS Big Data

MARCH 11, 2024

When running Apache Flink applications on Amazon Managed Service for Apache Flink , you have the unique benefit of taking advantage of its serverless nature. With Managed Service for Apache Flink, you can add and remove compute with the click of a button. Each KPU also comes with 50 GB of storage attached to the application.

Management 99

Management Snapshot Metrics Cost-Benefit 99

O'Reilly on Data

OCTOBER 30, 2023

While I am heartened to hear that the Executive Order on AI uses the Defense Production Act to compel disclosure of various data from the development of large AI models, these disclosures do not go far enough. These include: What data sources the model is trained on. Operational Metrics. Policy on use of user data for further training.

Measurement 293

Measurement Risk Modeling Metrics 293

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). But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools.

Management 327

Management Machine Learning Experimentation Metrics 327

DataKitchen

JUNE 1, 2022

Quality/Tests/Trust. How many tests do I have in production? T ools/Models/Dashboards. Is my model still accurate? What is the average number of tests per pipeline? How many models and dashboards were deployed? Testing/Impact/Regressions. How many tests ran in the QA environment? Root Cause.

Risk 130

Risk Management Testing Dashboards 130

IBM Big Data Hub

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications. What are large language models?

Modeling 86

Modeling Enterprise Software Machine Learning 86

Smart Data Collective

OCTOBER 13, 2022

Generative design is a new approach to product development that uses artificial intelligence to generate and test many possible designs. Automated Testing of Features. When creating a product or service, an organization may need to test its features. It involves tasks such as inspection, testing, and evaluation.

Testing 112

Testing Cost-Benefit Experimentation Optimization 112

IBM Big Data Hub

JANUARY 3, 2024

Most everyone has heard of large language models, or LLMs, since generative AI has entered our daily lexicon through its amazing text and image generating capabilities, and its promise as a revolution in how enterprises handle core business functions. Enter the world of Large Speech Models, or LSMs. But there’s more.

Modeling 102

Modeling Interactive Metrics Testing 102

CIO Business Intelligence

JULY 11, 2023

In recent years, we have witnessed a tidal wave of progress and excitement around large language models (LLMs) such as ChatGPT and GPT-4. On the contrary, the software can still be deployed with one click on any public or private cloud, managed, and scaled accordingly.

Modeling 71

Modeling Testing Experimentation Risk Management 71

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Are production models accurate, and do dashboards display correct data? Is My Model Still Accurate? How Many Tests Do I Have In Production?

Testing 124

Testing Metrics Data Quality Dashboards 124

Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

IBM Big Data Hub

SEPTEMBER 18, 2023

How do you secure AI models? The challenge with securing AI models stems not only from the underlying data’s dynamic nature and volume, but also the extended “attack surface” that AI models introduce: an attack surface that is new to all. And security is typically viewed as one of these unknowns.

Modeling 82

Modeling Enterprise Testing Cost-Benefit 82

CIO Business Intelligence

SEPTEMBER 7, 2023

That’s because the current generation of AI is already very good at two things needed in supply chain management. Some of what they do is used in our fabs for inspection and testing, but sometimes they develop AI that’s delivered inside our products, without anybody necessarily knowing about it.”

Management 137

Management Forecasting Manufacturing Cost-Benefit 137

O'Reilly on Data

OCTOBER 19, 2021

Since software engineers manage to build ordinary software without experiencing as much pain as their counterparts in the ML department, it begs the question: should we just start treating ML projects as software engineering projects as usual, maybe educating ML practitioners about the existing best practices? Why did something break?

IT 342

IT Testing Experimentation Software 342

CIO Business Intelligence

JUNE 7, 2022

It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more.

Data Analytics 129

Data Analytics Diagnostic Analytics Management Analytics 129

O'Reilly on Data

OCTOBER 18, 2022

What if a named entity recognition (NER) system, based on a cutting-edge large language model (LLM), fails for Chinese, Cyrillic, or Arabic text? As AI technologies are adopted more broadly in security and other high-risk applications, we’ll all need to know more about AI audit and risk management. being the only law firm of the bunch.

Risk Management 287

Risk Management Risk Testing Measurement 287

Ontotext

FEBRUARY 1, 2024

We as a taxonomy community are facing rapid technological change and grappling with where our discipline fits into a world captivated by ChatGPT and other generative models. The challenge of complexity Knowledge models for taxonomy have responded to an increasing need for sophistication and expressiveness.

Enterprise 59

Enterprise Management Metadata Modeling 59

CIO Business Intelligence

APRIL 22, 2022

Behind every successful IT project, you’ll find a highly skilled project manager. From hardware and software upgrades to ongoing security patches, to application development and the rollout of software itself, project managers keep your teams on task and productive. Top project management certifications.

Management 109

Management Cost-Benefit Testing Risk 109

CIO Business Intelligence

NOVEMBER 17, 2022

A key component to the appeal of cloud-first business models is cloud technologies’ ability to simplify processes and streamline workflows through integration and automation. This is especially true for content management operations looking to navigate the complexities of data compliance while getting the most from their data.

Software 135

Software Management Digital Transformation Data Governance 135