KDnuggets

SEPTEMBER 20, 2022

When building and optimizing your classification model, measuring how accurately it predicts your expected outcome is crucial. However, this metric alone is never the entire story, as it can still offer misleading results.

Metrics 160

Metrics Measurement Optimization Modeling 160

CIO Business Intelligence

MAY 24, 2024

Adopting a Managed Device Services model significantly reduces many of these end-of-life headaches and helps ensure compliance. A key element for governance goals will be a well-informed IT life-cycle management strategy that includes a comprehensive decommissioning policy, which reduces security risks and improves sustainability metrics.

Management 108

Management IT Cost-Benefit Metrics 108

Domino Data Lab

MARCH 3, 2021

It’s often stated that nothing changes inside an enterprise because you’ve built a model. In some cases, data science does generate models directly to revenue, such as a contextual deal engine that targets people with offers that they can instantly redeem. But what about good decisions?

Metrics 93

Metrics Modeling Sales Data Science 93

DataKitchen

MARCH 16, 2023

But wait, she asks you for your team metrics. Where is your metrics report? What are the metrics that matter? Gartner attempted to list every metric under the sun in their recent report , “T oolkit: Delivery Metrics for DataOps, Self-Service Analytics, ModelOps, and MLOps, ” published February 7, 2023.

Metrics 130

Metrics Data Analytics Analytics Measurement 130

datapine

SEPTEMBER 29, 2022

6) Data Quality Metrics Examples. Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. Table of Contents. 1) What Is Data Quality Management? 2) Why Do You Need DQM? 3) The 5 Pillars of DQM. 4) Data Quality Best Practices.

Data Quality 241

Data Quality Metrics Data-driven Management 241

datapine

AUGUST 14, 2020

A customer retention dashboard and metrics depicted in a neat visual will help you in monitoring, analyzing, and managing multiple customer-centric points and how they echo in your business. But first, let’s start with a basic definition. Your Chance: Want to build a dashboard for customer retention?

Dashboards 176

Dashboards Metrics B2B Visualization 176

CIO Business Intelligence

JUNE 4, 2024

On the pro-code front, Andreas Welsch, VP and head of AI marketing, said in an interview that SAP is leveraging its partnership with Nvidia to fine tune an LLM model on ABAP code. Also later this year, customers will be able to make natural language requests for insights into processes that will generate charts and metrics.

IT 109

IT Data Governance ROI Business Objectives 109

IBM Big Data Hub

MAY 17, 2024

Cloud maturity models are a useful tool for addressing these concerns, grounding organizational cloud strategy and proceeding confidently in cloud adoption with a plan. Cloud maturity models are a useful tool for addressing these concerns, grounding organizational cloud strategy and proceeding confidently in cloud adoption with a plan.

Modeling 97

Modeling Cost-Benefit Optimization Digital Transformation 97

Smart Data Collective

FEBRUARY 22, 2022

More companies than ever are shifting towards digital business models. They have access to troves of valuable data, which can be used to improve the profitability of their business models. Keep reading to learn more about the metrics that data-driven online stores are prioritizing. 1 – Customer acquisition costs.

Data-driven 129

Data-driven Metrics Sales Big Data 129

Jedox

JULY 6, 2021

When considering the performance of any forecasting model, the prediction values it produces must be evaluated. This is done by calculating suitable error metrics. An error metric is a way to quantify the performance of a model and provides a way for the forecaster to quantitatively compare different models 1.

Forecasting 104

Forecasting Metrics Testing Modeling 104

CIO Business Intelligence

APRIL 25, 2024

During the summer of 2023, at the height of the first wave of interest in generative AI, LinkedIn began to wonder whether matching candidates with employers and making feeds more useful would be better served with the help of large language models (LLMs). The initial deliverables “felt lacking,” Bottaro said.

IT 139

IT Experimentation Cost-Benefit Metrics 139

CIO Business Intelligence

MAY 7, 2024

Red hat announced refinements to the Ansible Lightspeed service today that make the code recommendations more relevant, including: Model customization/tuning. Users can now use their existing Ansible content with the IBM watsonx Code Assistant to train the model. Enhancements to Ansible Lightspeed are generally available now.

IT 130

IT Dashboards Enterprise Data Processing 130

Smart Data Collective

SEPTEMBER 21, 2021

A growing number of businesses are starting to look for new data-driven approaches to streamline their business models. However, your data-driven business model won’t be very helpful if you don’t focus on the right metrics. Targeting the Right Variables for Your Data-Driven Retail Business Model.

Data-driven 107

Data-driven Metrics Big Data Sales 107

CIO Business Intelligence

APRIL 10, 2024

The company needs massive computing power with CPUs and GPUs that are optimized for AI development, says Clark, adding that Seekr looked at the infrastructure it would need to build and train its huge AI models and quickly determined that buying and maintaining the hardware would be prohibitively expensive. Clark says.

IT 117

IT Testing Optimization Modeling 117

Cloudera

NOVEMBER 29, 2023

That’s why we’re excited to announce the Cloudera Model Registry as generally available, a game-changer that’s set to transform the way you manage your machine learning models in production environments. That’s where the Model Registry steps in. That’s where the Model Registry steps in.

Modeling 90

Modeling Machine Learning Metadata Metrics 90

Cloudera

JULY 21, 2022

Cloudera has partnered with Rill Data, an expert in metrics at any scale, as Cloudera’s preferred ISV partner to provide technical expertise and support services for Apache Druid customers. Deploying metrics shouldn’t be so hard. Rill solves this with pipeline services and Rill Developer, a free SQL-based data modeler.

Metrics 89

Metrics Slice and Dice Data Warehouse Dashboards 89

Smart Data Collective

AUGUST 16, 2021

Analytics data can be very useful for companies trying to create profitable online business models. Pay attention to the following metrics in your analytics dashboard to help you achieve greater success with your store. This metric is the average number you have to put in to get new customers. Audience Information.

Metrics 129

Metrics Analytics Cost-Benefit Sales 129

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 79

Management IT Machine Learning Metrics 79

CIO Business Intelligence

SEPTEMBER 5, 2023

Which is why many wise CIOs create an IT Business Office and make it responsible for alleviating the administrative burden for the IT organization, taking on responsibilities like the following: IT’s performance metrics and reporting systems: Doing metrics right isn’t easy. Just formulating them well takes a lot of effort.

IT 117

IT Metrics Statistics Management 117

AWS Big Data

JUNE 27, 2023

Our previous solution offered visualization of key metrics, but point-in-time snapshots produced only in PDF format. In this post, we discuss how we built a solution using QuickSight that delivers real-time visibility of key metrics to public sector recruiters.

Metrics 98

Metrics Dashboards Interactive Visualization 98

Analytics Vidhya

DECEMBER 16, 2020

The post Decluttering the performance measures of classification models appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Introduction There are so many performance evaluation measures when it comes to.

Measurement 296

Measurement Modeling Data Science Publishing 296

CIO Business Intelligence

DECEMBER 19, 2023

Embrace metrics and iterate To achieve maximum efficiency, Cziomer also suggests focusing service efforts on DevOps Research and Assessment (DORA) metrics, such as “lead time for change” and “time to restore service.” Service delivery excellence is an attribute that far too many IT leaders fail to prioritize.

IT 139

IT Testing Metrics Optimization 139

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.

Modeling 100

Modeling Interactive Metrics Testing 100

Domino Data Lab

OCTOBER 16, 2019

This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model reliability, consistency and performance in the future. Machine learning models: running wild. They may also experience relief when the model “works” in production.

Metrics 49

Metrics Modeling Machine Learning Measurement 49

DataKitchen

OCTOBER 28, 2021

Machine learning (ML) models are computer programs that draw inferences from data — usually lots of data. One way to think of ML models is that they instantiate an algorithm (a decision-making procedure often involving math) in software and then, at relatively low cost, deploy it on a large scale. What Is AI Bias?

Testing 130

Testing IT Manufacturing Metrics 130

DataRobot Blog

JUNE 27, 2022

In our previous two posts, we discussed extensively how modelers are able to both develop and validate machine learning models while following the guidelines outlined by the Federal Reserve Board (FRB) in SR 11-7. Monitoring Model Metrics.

Risk 59

Risk Modeling Metrics Measurement 59

CIO Business Intelligence

SEPTEMBER 15, 2023

In August, Salesforce released a new no-code , interface-based AI and generative AI model training tool, dubbed Einstein Studio , as part of its Data Cloud offering. Salesforce sees boomerang hires as a new success metric, the top executives said, adding that there still might be strategic layoffs in the future.

IT 131

IT Uncertainty Sales Data-driven 131

Smart Data Collective

APRIL 1, 2020

Big data is the lynchpin of the Software as a Service (SaaS) business model. Panoply, a big data innovator, has talked about a new model called Data as a Service, which is a variation of the same idea. Big data is used at every junction of the SaaS model. SaaS Model Relies on Big Data Innovation. So, are you ready?

Metrics 100

Metrics Big Data Machine Learning Optimization 100

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 362

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

NOVEMBER 21, 2023

Ask IT leaders about their challenges with shadow IT, and most will cite the kinds of security, operational, and integration risks that give shadow IT its bad rep. That’s not to downplay the inherent risks of shadow IT. There are ample reasons why 77% of IT professionals are concerned about shadow IT, according to a report from Entrust.

IT 137

IT Risk Cost-Benefit Risk Management 137

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.

Measurement 297

Measurement Risk Modeling Metrics 297

CIO Business Intelligence

MAY 2, 2023

One recent study shows that only 50% follow a product-centric operating model focusing on customer centricity and delivering delightful customer experiences. CIOs must communicate strategy and goals when making investment cases and garnering support from the CEO, executives, and the board. CIOs must facilitate a discussion on priorities.

Digital Transformation 141

Digital Transformation IT Data-driven Metrics 141

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 191

Machine Learning Modeling Testing Risk Management 191

DataKitchen

MAY 10, 2024

The Challenge of Data Production Data production encompasses processing and refining raw data into valuable insights, including dashboard production, warehouse constructions, and model refreshes. Are production models accurate, and do dashboards display correct data? Is My Model Still Accurate?

Testing 124

Testing Metrics Data Quality Dashboards 124

DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

DataRobot

FEBRUARY 10, 2021

Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. These errors might seem small, but the effects can be disastrous when the model is used to make decisions in the real world.

Modeling 98

Modeling Deep Learning Machine Learning Testing 98

Domino Data Lab

SEPTEMBER 10, 2020

Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant. What’s going on? Boosting your odds of success.

Modeling 101

Modeling IT Data Science Experimentation 101