AWS Big Data

JULY 14, 2023

To collect that information, Bob gets in touch with the head of each department, who in turn refer him to their development leads, who in turn give him a bunch of technical documents that explain how APIs are being used. The model should be able to showcase to LOBs their categories and capabilities.

Modeling 71

Modeling Visualization Reporting Measurement 71

O'Reilly on Data

JUNE 15, 2023

The world changed on November 30, 2022 as surely as it did on August 12, 1908 when the first Model T left the Ford assembly line. If we want prosocial outcomes, we need to design and report on the metrics that explicitly aim for those outcomes and measure the extent to which they have been achieved.

Metrics 292

Metrics Reporting Measurement Finance 292

datapine

NOVEMBER 15, 2022

Product metrics are quantitative performance measurements used by businesses to gain insights into various areas like product development, pricing, quality, and more. They can evaluate architectural measures, quality measures, software complexity, or functional size. What Are Product Metrics? Let’s dive into them.

Metrics 141

Metrics KPI Measurement Dashboards 141

IBM Big Data Hub

APRIL 13, 2023

After developing a machine learning model, you need a place to run your model and serve predictions. If your company is in the early stage of its AI journey or has budget constraints, you may struggle to find a deployment system for your model. Also, a column in the dataset indicates if each flight had arrived on time or late.

Modeling 81

Modeling Statistics Machine Learning Testing 81

IBM Big Data Hub

OCTOBER 16, 2023

AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Foundation models can use language, vision and more to affect the real world.

Risk 70

Risk Modeling Management Metadata 70

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Domino Data Lab

JULY 17, 2021

The system here will identify, via some meaningful sense, which existing speakers’ model does the utterance match. If the unknown utterance is spoken by a speaker outside the list of existing speakers, the model will nonetheless map it to some speaker from that list. The existing applications of person authentication include :-.

Modeling 64

Modeling Testing Machine Learning Measurement 64

O'Reilly on Data

JULY 28, 2020

These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ). Any metric can and will be abused.

Marketing 363

Marketing Experimentation Metrics Testing 363

datapine

SEPTEMBER 29, 2022

5) How Do You Measure Data Quality? In this article, we will detail everything which is at stake when we talk about DQM: why it is essential, how to measure data quality, the pillars of good quality management, and some data quality control techniques. These needs are then quantified into data models for acquisition and delivery.

Data Quality 241

Data Quality Metrics Data-driven Management 241

DataKitchen

MARCH 16, 2023

How to measure your data analytics team? Under Velocity, the Mean Time to Deliver Data metric measures the time it takes to deliver data. The Data Change Request Ratio metric measures the rate of business demand for data. The Mean Time to Recovery metric measures how quickly defects can be resolved. Introduction.

Metrics 130

Metrics Data Analytics Analytics Measurement 130

Domino Data Lab

JULY 1, 2021

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. How to build a statistical Named Entity Recognition (NER) model.

Modeling 111

Modeling Statistics Testing Metrics 111

DataRobot

AUGUST 25, 2021

In a recent blog, we talked about how, at DataRobot , we organize trust in an AI system into three main categories: trust in the performance in your AI/machine learning model , trust in the operations of your AI system, and trust in the ethics of your modelling workflow, both to design the AI system and to integrate it with your business process.

Metrics 98

Metrics Measurement Machine Learning Data Collection 98

DataKitchen

SEPTEMBER 20, 2021

For example, data cleansing, ETL, running a model, or even provisioning cloud infrastructure. The DataKitchen Platform automatically updates the embedded references from one toolchain instance to the other. Meta-orchestration refers to the integration of observability into a hierarchy of orchestrations. Measurement DataOps.

Testing 246

Testing Metrics Measurement Dashboards 246

Domino Data Lab

JANUARY 20, 2019

What if there was a way to quantitatively measure whether your machine learning (ML) model reflects specific domain expertise or potential bias? Using high-level concepts is potentially useful when translating seemingly black box models to stakeholders that are not experts in ML. Introduction. with post-training explanations?

Testing 63

Testing Modeling Machine Learning Data Science 63

CIO Business Intelligence

MAY 29, 2024

other material descriptions including fractions, units of measure, units of sale, etc. A search engine using AI would recognize that the differences between the query and the material description are referring to the same product. DIA steel pipe”. While rules can be written to explicitly convert “feet” to “ft.”

Sales 77

Sales Modeling Machine Learning Measurement 77

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

AWS Big Data

JANUARY 9, 2024

Traditional lexical search, based on term frequency models like BM25, is widely used and effective for many search applications. Semantic search In semantic search, the search engine uses an ML model to encode text or other media (such as images and videos) from the source documents as a dense vector in a high-dimensional vector space.

Visualization 92

Visualization Cost-Benefit Modeling Machine Learning 92

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps is a term that refers to the set of practices and tools that organizations use to improve the quality and speed of data analytics and machine learning. The goal of DataOps is to help organizations make better use of their data to drive business decisions and improve outcomes. Query> Write an essay on DataOps.

Machine Learning 130

Machine Learning Data-driven Optimization Modeling 130

DataKitchen

SEPTEMBER 21, 2023

Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. Because let’s face it, your customers don’t care where the problem originated—they want it fixed and fast. What is Data in Place?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

DataRobot

JUNE 17, 2021

At the time, I had a small following of people interested in using Eureqa to derive mathematical formulas and models. Traditionally, science has advanced in many cases by having brilliant researchers compete different hypotheses to explain experimental data, and then design experiments to measure which is correct.

Modeling 52

Modeling Experimentation Machine Learning Measurement 52

IBM Big Data Hub

APRIL 15, 2024

To understand human behavior and how to change it, there are a few factors we must assess and measure, supported by the COM-B Behavior Change Wheel. This refers to their knowledge and skills to engage in safe online practices, such as creating strong passwords and recognizing phishing attempts.

Metrics 86

Metrics Measurement Risk Data Processing 86

AWS Big Data

FEBRUARY 29, 2024

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. Enterprises are doing this by using proprietary data with approaches like Retrieval Augmented Generation (RAG), fine-tuning, and continued pre-training with foundation models.

Data Governance 95

Data Governance Unstructured Data Metadata Data Lake 95

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

Occam's Razor

AUGUST 12, 2013

than multi-channel attribution modeling. By the time you are done with this post you'll have complete knowledge of what's ugly and bad when it comes to attribution modeling. You'll know how to use the good model, even if it is far from perfect. Multi-Channel Attribution Models. Linear Attribution Model.

Modeling 161

Modeling Optimization Marketing Interactive 161

CIO Business Intelligence

MAY 16, 2024

Smart buildings, sensors and building management systems are used to collect such building data, and the IT team starts with quality digital design and management, with building information modeling (BIM) and digital twins. A framework in the making As in other areas, the EU has provided a regulatory framework on sustainability.

Digital Transformation 88

Digital Transformation Data-driven Cost-Benefit Management 88

AWS Big Data

AUGUST 7, 2023

Amazon Redshift ML makes it easy for data analysts and database developers to create, train, and apply machine learning (ML) models using familiar SQL commands in Amazon Redshift. Simply use SQL statements to create and train SageMaker ML models using your Redshift data and then use these models to make predictions.

Forecasting 84

Forecasting Data Warehouse Machine Learning Modeling 84

AWS Big Data

JULY 28, 2023

Data management and governance Addressing the challenges mentioned requires a combination of technical, operational, and legal measures. For more information about tagging, refer to Tagging resources in Amazon Redshift. Refer to AWS Lake Formation-managed Redshift shares for more details on the implementation.

Snapshot 83

Snapshot Metadata Measurement Data Warehouse 83

Occam's Razor

APRIL 2, 2012

Included in the post are recommendations for measurement and data analysis. And as if that was not enough, :), I'll close the post with my thoughts on digital marketing attribution models. We'll refer to this quest for doing effective attribution as MCA-O2S. This is going to be a lot of fun.

Modeling 152

Modeling Advertising Marketing Measurement 152

AWS Big Data

MARCH 7, 2023

It covers how to use a conceptual, logical architecture for some of the most popular gaming industry use cases like event analysis, in-game purchase recommendations, measuring player satisfaction, telemetry data analysis, and more. The following diagram is a conceptual analytics data hub reference architecture.

Analytics 71

Analytics Data Warehouse Data Lake Metadata 71

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. All predictive models are wrong at times?—just

Risk 361

Risk Modeling Data Processing Software 361

Smart Data Collective

MAY 29, 2023

Understanding Bias in AI Translation Bias in AI translation refers to the distortion or favoritism present in the output results of machine translation systems. Labeling Bias: Impact on Model Performance The presence of labeling bias in AI translation systems will significantly impact the model’s performance.

Metrics 69

Metrics Measurement Data Collection Strategy 69

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Rocket-Powered Data Science

JULY 7, 2019

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: Cars, Trucks, Taxis. See [link]. Industry 4.0 Industry 4.0 2) Connected cars. (3)

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

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? Dimensions of Trust. How large is the data set?

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

IBM Big Data Hub

FEBRUARY 5, 2024

Numerous individuals disclose confidential, personal data to commercial and public entities daily, necessitating that government institutions uphold stringent security measures to protect their assets. Reference FICAM architecture The provided figure is a reference architecture to highlight necessary pieces about FICAM implementation.

Risk 86

Risk Cost-Benefit Measurement Management 86

Jet Global

AUGUST 24, 2021

Enter scenario modeling. Scenario modeling is the practice of developing financial models based on several possible outcomes, and developing plans around each of those situations. Let’s look at some of the best practices for financial scenario modeling. How detailed should your scenario models be?

Modeling 52

Modeling Recreation/Entertainment Forecasting Sales 52

CIO Business Intelligence

AUGUST 30, 2023

As an example, the consultancy refers to how generative AI technology could potentially add $200 – $400 billion in added annual value to the banking industry if full implementation moves ahead on various use cases. Maybe it gets used in modeling. People send things into ChatGPT that they shouldn’t, now stored in ChatGPT servers.

Enterprise 128

Enterprise Risk Measurement Software 128