Smart Data Collective

JANUARY 16, 2023

Companies around the world are projected to spend over $300 billion on machine learning technology by 2030. There are a growing number of reasons that companies are investing in machine learning, but digital marketing is at the top of the list. SEO, in particular, relies more heavily on machine learning these days.

Machine Learning 134

Machine Learning Marketing Cost-Benefit Optimization 134

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). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 363

Management Machine Learning Experimentation Metrics 363

David Menninger's Analyst Perspectives

MAY 20, 2025

Dedicated planning and budgeting software has been around for decades but is about to become all the more useful as software providers increasingly incorporate artificial intelligence (AI) using machine learning (ML) to assist in scenario planning. Business is all about managing trade-offs because resources are always limited.

Forecasting 130

Forecasting Software Finance Uncertainty 130

CIO Business Intelligence

JANUARY 6, 2025

Wetmur says Morgan Stanley has been using modern data science, AI, and machine learning for years to analyze data and activity, pinpoint risks, and initiate mitigation, noting that teams at the firm have earned patents in this space. Its more about optimizing and maximizing the value were getting out of gen AI, she says.

IT 140

IT Cost-Benefit Measurement Experimentation 140

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 123

Insurance Machine Learning Cost-Benefit Risk 123

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Why aren’t traditional software tools sufficient?

Modeling 256

Modeling Machine Learning Testing Metrics 256

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

O'Reilly on Data

MAY 14, 2020

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. In addition, the Research PM defines and measures the lifecycle of each research product that they support.

Management 364

Management Experimentation B2B Machine Learning 364

AWS Big Data

JANUARY 15, 2025

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Improve accuracy and resiliency of analytics and machine learning by fostering data standards and high-quality data products.

IoT 110

IoT Machine Learning Metadata Data-driven 110

Cloudera

NOVEMBER 17, 2021

We are very excited to announce the release of five, yes FIVE new AMPs, now available in Cloudera Machine Learning (CML). In addition to the UI interface, Cloudera Machine Learning exposes a REST API that can be used to programmatically perform operations related to Projects, Jobs, Models, and Applications.

Machine Learning 124

Machine Learning Visualization Data Science Dashboards 124

O'Reilly on Data

SEPTEMBER 15, 2020

Technical sophistication: Sophistication measures a team’s ability to use advanced tools and techniques (e.g., PyTorch, TensorFlow, reinforcement learning, self-supervised learning). Technical competence: Competence measures a team’s ability to successfully deliver on initiatives and projects. Conclusion.

Advertising 362

Advertising Cost-Benefit ROI Machine Learning 362

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. . Collaboration and Sharing.

Testing 304

Testing Machine Learning Consulting Data Science 304

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

CIO Business Intelligence

OCTOBER 4, 2022

From delightful consumer experiences to attacking fuel costs and carbon emissions in the global supply chain, real-time data and machine learning (ML) work together to power apps that change industries. more machine learning use casesacross the company. By Bryan Kirschner, Vice President, Strategy at DataStax.

Machine Learning 118

Machine Learning Data Architecture Uncertainty Data-driven 118

Smart Data Collective

FEBRUARY 23, 2023

By analyzing data and extracting useful insights, brands can make informed decisions to optimize their branding strategies. This article will explore data mining and how it can help online brands with brand optimization. Conclusion Data mining is a powerful tool for online brands looking to optimize their branding strategies.

Data mining 145

Data mining Optimization Technology Marketing 145

O'Reilly on Data

MARCH 25, 2025

People have been building data products and machine learning products for the past couple of decades. How will you measure success? So now we have a user persona, several scenarios, and a way to measure success. Slow response/high cost : Optimize model usage or retrieval efficiency. This isnt anything new.

Testing 177

Testing Data-driven Software Measurement 177

O'Reilly on Data

NOVEMBER 19, 2024

decomposes a complex task into a graph of subtasks, then uses LLMs to answer the subtasks while optimizing for costs across the graph. presented the TRACE framework for measuring results, which showed how GraphRAG achieves an average performance improvement of up to 14.03%. For example, “ Graph of Thoughts ” by Maciej Besta, et al.,

Unstructured Data 321

Unstructured Data Structured Data Statistics Modeling 321

Cloudera

FEBRUARY 11, 2022

Advances in the development and application of Machine Learning (ML) and Deep Learning (DL) algorithms, require greater care to ensure that the ethics embedded in previous rule-based systems are not lost. What is Machine Learning. Instead, they are learned by training a model on data.

Machine Learning 97

Machine Learning Sales Data Science Deep Learning 97

Rocket-Powered Data Science

DECEMBER 15, 2018

This wisdom applies not only to life but to machine learning also. Specifically, the availability and application of labeled data (things past) for the labeling of previously unseen data (things future) is fundamental to supervised machine learning. A related problem also arises in unsupervised machine learning.

Machine Learning 171

Machine Learning Optimization Metrics Deep Learning 171

CIO Business Intelligence

DECEMBER 20, 2024

Invest in core functions that perform data curation such as modeling important relationships, cleansing raw data, and curating key dimensions and measures. Optimize data flows for agility. Limit the times data must be moved to reduce cost, increase data freshness, and optimize enterprise agility. AI and machine learning models.

Data Architecture 122

Data Architecture Management Consulting Internet of Things 122

Smart Data Collective

FEBRUARY 9, 2022

Learn how DirectX visualization can improve your study and assessment of different trading instruments for maximum productivity and profitability. A growing number of traders are using increasingly sophisticated data mining and machine learning tools to develop a competitive edge.

Visualization 145

Visualization Optimization Analytics Testing 145

Smart Data Collective

JUNE 24, 2023

Fortunately, new advances in machine learning technology can help mitigate many of these risks. Therefore, you will want to make sure that your cryptocurrency wallet or service is protected by machine learning technology. In 2018, researchers used data mining and machine learning to detect Ponzi schemes in Ethereum.

Machine Learning 98

Machine Learning Broadcasting Risk Data mining 98

O'Reilly on Data

AUGUST 4, 2022

We can find many more examples across many more decades that reflect naiveté and optimism and–if we are honest–no small amount of ignorance and hubris. This kind of humility is likely to deliver more meaningful progress and a more measured understanding of such progress.

Machine Learning 341

Machine Learning Modeling Statistics Software 341

datapine

NOVEMBER 27, 2019

Often seen as the highest foe-friend of the human race in movies ( Skynet in Terminator, The Machines of Matrix or the Master Control Program of Tron), AI is not yet on the verge to destroy us, in spite the legit warnings of some reputed scientists and tech-entrepreneurs. 5) Collaborative Business Intelligence.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI In this post, we shed some light on various efforts toward generating data for machine learning (ML) models. Machine learning applications rely on three main components: models, data, and compute.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

CIO Business Intelligence

SEPTEMBER 26, 2023

But because electricity consumption was easy to gauge, there was no urgency for measuring current and low voltage power flows. But the measuring solution was complex and required frequent manual adaptions as solar PV systems increased. Without real-time power measurements, estimated power values were being used.

Machine Learning 105

Machine Learning Forecasting Measurement Data Warehouse 105

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 also talked about the increased interest in green AI, in which we not only measure the quality of a model based on accuracy but also how big and complex it is.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

CIO Business Intelligence

FEBRUARY 20, 2025

In addition, they can use statistical methods, algorithms and machine learning to more easily establish correlations and patterns, and thus make predictions about future developments and scenarios. Most use master data to make daily processes more efficient and to optimize the use of existing resources.

Big Data 84

Big Data Measurement Visualization Machine Learning 84

O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. However, the concept is quite abstract.

IT 364

IT Testing Experimentation Software 364

CIO Business Intelligence

MAY 23, 2024

More recently, products have become increasingly digital, with software that manages patient flows, tools for surgery planning, and sterile management processes that optimize inventory and ensure that surgical instruments are delivered at the right time to the right place.

Digital Transformation 111

Digital Transformation Measurement Cost-Benefit Data Warehouse 111

DataKitchen

APRIL 26, 2021

Workiva also prioritized improving the data lifecycle of machine learning models, which otherwise can be very time consuming for the team to monitor and deploy. Workiva uses a broad range of metrics to measure success. Hodges shared that he was inspired to learn more about lean principles.

Measurement 246

Measurement Metrics Data-driven Dashboards 246

Smart Data Collective

SEPTEMBER 30, 2021

This article will help you to understand how remote working has caused cybercrime, its consequences, and proactive measures focusing on AI-driven cybersecurity apps to handle this critical issue. Optimizing AI-Driven Cybersecurity Apps. It indicates that businesses should do everything they can to protect their critical data.

Optimization 135

Optimization IoT Data-driven Cost-Benefit 135

DataKitchen

NOVEMBER 5, 2024

Data is typically organized into project-specific schemas optimized for business intelligence (BI) applications, advanced analytics, and machine learning. By starting with testing and measurements, even before standards are fully established, organizations can build a foundation for continuous improvement.

Data Quality 198

Data Quality Testing Metrics Reporting 198

AWS Big Data

DECEMBER 7, 2023

In this post, we outline planning a POC to measure media effectiveness in a paid advertising campaign. We chose to start this series with media measurement because “Results & Measurement” was the top ranked use case for data collaboration by customers in a recent survey the AWS Clean Rooms team conducted. and CTV.Co

Measurement 115

Measurement Advertising Testing Data-driven 115

Rocket-Powered Data Science

JULY 21, 2023

Organizations are able to monitor integrity, quality drift, performance trends, real-time demand, SLA (service level agreement) compliance metrics, and anomalous behaviors (in devices, applications, and networks) to provide timely alerting, early warnings, and other confidence measures. “Don’t be a SOAR loser!

Machine Learning 190

Machine Learning Metrics Internet of Things Reporting 190

CIO Business Intelligence

APRIL 24, 2025

The cloud gives us greater flexibility and dynamism, so its part of the optimization of the platform were working with. Streamline and optimize The third major focus is to make SJ more efficient by optimizing its planning how time slots are allocated in relation to trains, staff, and different skills.

IT 71

IT Consulting Optimization IoT 71

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. blueberry spacing) is a measure of the model’s interpretability. Machine Learning Model Lineage. Machine Learning Model Visibility . Figure 04: Applied Machine Learning Prototypes (AMPs).

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85