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

O'Reilly on Data

MARCH 25, 2025

Throughout this article, well explore real-world examples of LLM application development and then consolidate what weve learned into a set of first principlescovering areas like nondeterminism, evaluation approaches, and iteration cyclesthat can guide your work regardless of which models or frameworks you choose. Which multiagent frameworks?

Testing 174

Testing Data-driven Software Measurement 174

O'Reilly on Data

NOVEMBER 19, 2024

Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. See the primary sources “ REALM: Retrieval-Augmented Language Model Pre-Training ” by Kelvin Guu, et al., at Facebook—both from 2020.

Unstructured Data 318

Unstructured Data Structured Data Statistics Modeling 318

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. These needs are then quantified into data models for acquisition and delivery. Table of Contents. 1) What Is Data Quality Management?

Data Quality 241

Data Quality Metrics Data-driven Management 241

CIO Business Intelligence

OCTOBER 10, 2024

The growing importance of ESG and the CIO’s role As business models become more technology-driven, the CIO must assume a leadership role, actively shaping how technologies like AI, genAI and blockchain contribute to meeting ESG targets. Similarly, blockchain technologies have faced scrutiny for their energy consumption.

Metrics 124

Metrics Optimization Strategy Technology 124

Smart Data Collective

FEBRUARY 9, 2022

Luckily, there are a few analytics optimization strategies you can use to make life easy on your end. For instance, they display trend lines, pivot points, low volatility and other metrics in distinct colors. A powerful back-testing engine: It allows you to generate performance metrics for multiple stocks at the touch of a button.

Visualization 145

Visualization Optimization Analytics Testing 145

CIO Business Intelligence

MARCH 25, 2025

Using the companys data in LLMs, AI agents, or other generative AI models creates more risk. What CIOs can do: Avoid and reduce data debt by incorporating data governance and analytics responsibilities in agile data teams , implementing data observability , and developing data quality metrics.

Risk 123

Risk Cost-Benefit Data-driven Digital Transformation 123

Rocket-Powered Data Science

DECEMBER 16, 2018

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. See the related post for more details about the cold start challenge.

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

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 131

Data-driven Metrics Big Data Sales 131

AWS Big Data

DECEMBER 5, 2024

As the use of Hydro grows within REA, it’s crucial to perform capacity planning to meet user demands while maintaining optimal performance and cost-efficiency. Solution overview The MSK clusters in Hydro are configured with a PER_TOPIC_PER_BROKER level of monitoring, which provides metrics at the broker and topic levels.

Metrics 95

Metrics Dashboards Testing Optimization 95

datapine

NOVEMBER 15, 2022

1) What Are Product Metrics? 2) Types Of Product Metrics. 3) Product Metrics Examples You Can Use. 4) Product Metrics Framework. The right product performance metrics will give you invaluable insights into its health, strength and weaknesses, potential issues or bottlenecks, and let you improve it greatly.

Metrics 141

Metrics KPI Measurement Dashboards 141

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 363

Management Machine Learning Experimentation Metrics 363

Smart Data Collective

AUGUST 16, 2021

Crazy Egg, Google Analytics, Voluum and other analytics tools make it a lot easier to create a well-optimized and profitable business. Analytics data can be very useful for companies trying to create profitable online business models. This metric is the average number you have to put in to get new customers. Acquisition Costs.

Metrics 135

Metrics Analytics Cost-Benefit Sales 135

AWS Big Data

NOVEMBER 22, 2024

This upgrade allows you to build, test, and deploy data models in dbt with greater ease and efficiency, using all the features that dbt Cloud provides. This saves time and effort, especially for teams looking to minimize infrastructure management and focus solely on data modeling.

Data Lake 101

Data Lake Data Warehouse Cost-Benefit Data Transformation 101

O'Reilly on Data

MAY 14, 2020

Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model. In reality, many candidate models (frequently hundreds or even thousands) are created during the development process. Modelling: The model is often misconstrued as the most important component of an AI product.

Management 364

Management Experimentation B2B Machine Learning 364

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Before I continue, it’s important to emphasize that machine learning is much more than building models. Model lifecycle management.

Machine Learning 270

Machine Learning Risk Management Statistics 270

Smart Data Collective

SEPTEMBER 17, 2020

One of the most important benefits of data analytics with lead generation and optimization. Predictive analytics tools use a variety of scoring metrics to identify the probability that a lead will be converted into a paying customer. Monitor the Metrics. Big data is utilized in many facets of business.

Optimization 135

Optimization Data Analytics Analytics Sales 135

Domino Data Lab

JANUARY 11, 2021

Even for experienced developers and data scientists, the process of developing a model could involve stringing together many steps from many packages, in ways that might not be as elegant or efficient as one might like. the experience is still rooted in the same goal: simple efficiency for the whole model development lifecycle.

Modeling 145

Modeling Metrics Data Science Testing 145

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

Smart Data Collective

AUGUST 17, 2021

Many companies have found that analytics technology is ideal for optimizing their business models in a number of ways. The best way to measure and analyze the growth of a business is to use business metrics. There are a lot of data analytics tools that track these metrics and help businesses make more informed decisions.

Measurement 126

Measurement Optimization Analytics Metrics 126

AWS Big Data

AUGUST 21, 2024

To address this requirement, Redshift Serverless launched the artificial intelligence (AI)-driven scaling and optimization feature, which scales the compute not only based on the queuing, but also factoring data volume and query complexity. The slider offers the following options: Optimized for cost – Prioritizes cost savings.

Optimization 106

Optimization Data Lake Data Warehouse Cost-Benefit 106

AWS Big Data

NOVEMBER 19, 2024

Customers maintain multiple MWAA environments to separate development stages, optimize resources, manage versions, enhance security, ensure redundancy, customize settings, improve scalability, and facilitate experimentation. micro, remember to monitor its performance using the recommended metrics to maintain optimal operation.

Metadata 111

Metadata Cost-Benefit Metrics Optimization 111

CIO Business Intelligence

JANUARY 21, 2025

With traditional OCR and AI models, you might get 60% straight-through processing, 70% if youre lucky, but now generative AI solves all of the edge cases, and your processing rates go up to 99%, Beckley says. In 2024, departments and teams experimented with gen AI tools tied to their workflows and operating metrics.

Cost-Benefit 142

Cost-Benefit Data-driven Strategy Marketing 142

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Observe, optimize, and scale enterprise data pipelines. . A complete DataOps program will have a unified, system-wide view of process metrics using a common data store.

Testing 300

Testing Machine Learning Consulting Data Science 300

CIO Business Intelligence

DECEMBER 18, 2024

Our history is rooted in a traditional distribution model of marketing, selling, and shipping vendor products to our resellers. What were the technical considerations moving from a distribution model to a platform? These high-level metrics tie to every leaders objectives. This is crucial in a value-driven development model.

Digital Transformation 116

Digital Transformation Metrics Measurement ROI 116

CIO Business Intelligence

NOVEMBER 13, 2024

The company has already rolled out a gen AI assistant and is also looking to use AI and LLMs to optimize every process. One is going through the big areas where we have operational services and look at every process to be optimized using artificial intelligence and large language models. We’re doing two things,” he says.

IT 141

IT Consulting ROI Cost-Benefit 141

AWS Big Data

FEBRUARY 13, 2024

AWS Glue has made this more straightforward with the launch of AWS Glue job observability metrics , which provide valuable insights into your data integration pipelines built on AWS Glue. This post, walks through how to integrate AWS Glue job observability metrics with Grafana using Amazon Managed Grafana. Sign in to your workspace.

Metrics 125

Metrics Dashboards Visualization Key Performance Indicator 125

Cloudera

DECEMBER 4, 2024

To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput. The Cloudera AI Inference service is a highly scalable, secure, and high-performance deployment environment for serving production AI models and related applications.

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

datapine

FEBRUARY 4, 2020

Structure your metrics. As with any report you might need to create, structuring and implementing metrics that will tell an interesting and educational data-story is crucial in our digital age. That way you can choose the best possible metrics for your case. Regularly monitor your data. 1) Marketing CMO report.

Reporting 280

Reporting Marketing Advertising Metrics 280

Cloudera

NOVEMBER 13, 2024

Large Language Models (LLMs) will be at the core of many groundbreaking AI solutions for enterprise organizations. Here are just a few examples of the benefits of using LLMs in the enterprise for both internal and external use cases: Optimize Costs. The Need for Fine Tuning Fine tuning solves these issues.

Cost-Benefit 87

Cost-Benefit Data Processing Machine Learning Testing 87

datapine

DECEMBER 11, 2019

SaaS is a software distribution model that offers a lot of agility and cost-effectiveness for companies, which is why it’s such a reliable option for numerous business models and industries. AI optimizes business processes, increasing productivity and efficiency while automating repetitive tasks and supporting human capabilities.

Software 314

Software Cost-Benefit Data-driven Data Processing 314

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. Insights from ML models can be channeled through Amazon DataZone to inform internal key decision makers internally and external partners.

IoT 111

IoT Machine Learning Metadata Data-driven 111

Rocket-Powered Data Science

DECEMBER 15, 2018

Many unsupervised learning models can converge more readily and be more valuable if we know in advance which parameterizations are best to choose. If we cannot know that ( i.e., because it truly is unsupervised learning), then we would like to know at least that our final model is optimal (in some way) in explaining the data.

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

O'Reilly on Data

APRIL 23, 2024

Instead of continuing to deploy their attention optimization algorithms for their users’ and suppliers’ benefit, the tech giants began to use them to favor themselves. Some of those innovations, like Amazon’s cloud computing business, represented enormous new markets and a new business model. But over time, something went very wrong.

Advertising 338

Advertising Cost-Benefit Marketing Risk 338

CIO Business Intelligence

MARCH 19, 2025

Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams. However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive.

IT 59

IT Data Governance Data-driven Metrics 59

CIO Business Intelligence

JANUARY 21, 2025

Excessive infrastructure costs: About 21% of IT executives point to the high cost of training models or running GenAI apps as a major concern. million in 2026, covering infrastructure, models, applications, and services. Engage stakeholders: Work with finance and operations teams to align on budgets, shared goals, and success metrics.

Cost-Benefit 98

Cost-Benefit ROI Measurement Strategy 98

AWS Big Data

NOVEMBER 27, 2024

This enables the line of business (LOB) to better understand their core business drivers so they can maximize sales, reduce costs, and further grow and optimize their business. After the data is in Amazon Redshift, dbt models are used to transform the raw data into key metrics such as ticket trends, seller performance, and event popularity.

Data Warehouse 109

Data Warehouse Analytics Testing Modeling 109