DataKitchen

SEPTEMBER 18, 2024

DataOps introduces agility by advocating for: Measuring data quality early : Data quality leaders should begin measuring and assessing data quality even before perfect standards are in place. Early measurements provide valuable insights that can guide future improvements. Measuring and Refining : DataOps is an iterative process.

Scorecard 180

Scorecard Data Quality Measurement Testing 180

DataCamp

MAY 5, 2020

This tutorial will define statistical bias in a machine learning model and demonstrate how to perform the test on synthetic data.

Machine Learning 96

Machine Learning Statistics Testing Measurement 96

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. Energy usage and other environmental impacts.

Measurement 304

Measurement Risk Metrics Modeling 304

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

The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Researchers and practitioners have been using human-labeled data for many years, trying to understand all sorts of abstract concepts that we could not measure otherwise. That’s the focus of this blog post.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

CIO Business Intelligence

MARCH 6, 2024

What has IT’s role been in the transformation to a SaaS model? We built that end-to-end data model and process from scratch while we ran the old business. We knew we had a unique opportunity to build a new end-to-end architecture with a common AI-powered data model. Today, we’re a $1.6 Today, we’re a $1.6

Modeling 116

Modeling Management IT Metrics 116

CIO Business Intelligence

SEPTEMBER 10, 2024

The US Department of Commerce’s Bureau of Industry and Security (BIS) plans to introduce mandatory reporting requirements for developers of advanced AI models and cloud computing providers.

Reporting 131

Reporting Measurement Testing Risk 131

O'Reilly on Data

OCTOBER 12, 2021

Kevlin Henney and I were riffing on some ideas about GitHub Copilot , the tool for automatically generating code base on GPT-3’s language model, trained on the body of code that’s in GitHub. We know how to test whether or not code is correct (at least up to a certain limit). First, we wondered about code quality.

Testing 308

Testing Measurement Consulting Modeling 308

Occam's Razor

SEPTEMBER 15, 2011

Winners, well before they think data or tool, have a well structured Digital Marketing & Measurement Model. This article guides you in understanding the value of the Digital Marketing & Measurement Model (notice the repeated emphasis on Marketing, not just Measurement), and how to create one for yourself.

Measurement 166

Measurement Marketing Modeling Key Performance Indicator 166

CIO Business Intelligence

JANUARY 18, 2024

Recognizing this need, we have created a cutting-edge VSM maturity model. Drawing upon our extensive experience facilitating successful VSM initiatives within large-scale enterprises, this model serves as a compass, offering insights into their current VSM maturity and providing practical direction to advance and reap the additional benefits.

Digital Transformation 119

Digital Transformation Modeling Management Data-driven 119

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 197

Machine Learning Modeling Testing Risk Management 197

DataKitchen

SEPTEMBER 20, 2021

The best way to ensure error-free execution of data production is through automated testing and monitoring. The DataKitchen Platform enables data teams to integrate testing and observability into data pipeline orchestrations. Automated tests work 24×7 to ensure that the results of each processing stage are accurate and correct.

Testing 246

Testing Metrics Measurement Dashboards 246

David Menninger's Analyst Perspectives

NOVEMBER 19, 2024

As a result, many data teams were not as productive as they might be, with time and effort spent on manually troubleshooting data-quality issues and testing data pipelines. The ability to monitor and measure improvements in data quality relies on instrumentation.

Data Quality 130

Data Quality Dashboards Data-driven Software 130

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? Testing with Concept Activation Vectors (TCAV): The Zebra. Introduction. with post-training explanations? on a global level instead of a local level ?

Testing 63

Testing Modeling Machine Learning Data Science 63

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 290

Metrics Reporting Measurement Finance 290

Cloudera

OCTOBER 20, 2021

What are the key climate risk measurements and impacts? Stress testing was heavily scrutinized in the post 2008 financial crisis. In a BIS advisory report , it was highlighted that the stress testing scenarios used by the banks were insufficient to capture the extreme risks and fluctuations that were realized. Assess Variables.

Risk 79

Risk Modeling Risk Management Measurement 79

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 Metrics 98

IBM Big Data Hub

SEPTEMBER 7, 2023

Today we are announcing our latest addition: a new family of IBM-built foundation models which will be available in watsonx.ai , our studio for generative AI, foundation models and machine learning. Collectively named “Granite,” these multi-size foundation models apply generative AI to both language and code.

Modeling 101

Modeling Risk Unstructured Data Enterprise 101

CIO Business Intelligence

SEPTEMBER 26, 2024

Many developers say AI coding assistants make them more productive, but a recent study set forth to measure their output and found no significant gains. The study measured pull request (PR) cycle time, or the time to merge code into a repository, and PR throughput, the number of pull requests merged.

Measurement 145

Measurement Software Testing Data-driven 145

DataKitchen

SEPTEMBER 21, 2023

In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets. Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors.

Testing 173

Testing Data Quality Predictive Modeling Metrics 173

CIO Business Intelligence

NOVEMBER 19, 2024

Generative AI (GenAI) is rapidly emerging as a game changer for enterprises, but turning its potential into measurable value remains a significant challenge. Data privacy and compliance issues Failing: Mismanagement of internal data with external models can lead to privacy breaches and non-compliance with regulations.

Consulting 126

Consulting Cost-Benefit Experimentation Optimization 126

O'Reilly on Data

JUNE 18, 2024

Many farmers measure their yield in bags of rice, but what is “a bag of rice”? While RAG is conceptually simple—look up relevant documents and construct a prompt that tells the model to build its response from them—in practice, it’s more complex. Digital Green tests with “Golden QAs,” highly rated sets of questions and answers.

Testing 240

Testing Software Modeling Measurement 240

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 (or CMMs) are frameworks for evaluating an organization’s cloud adoption readiness on both a macro and individual service level.

Modeling 96

Modeling Cost-Benefit Optimization Digital Transformation 96

DataRobot

MAY 27, 2021

Michael earned his MD and PhD degrees from Emory University, and today his research combines mathematical and epidemiological models with high-throughput phage-display–based serological laboratory investigations. In particular, Mina believes that our failure to test effectively has allowed COVID-19 to spread.

Testing 98

Testing Machine Learning Measurement Publishing 98

CIO Business Intelligence

JULY 24, 2024

Large language models (LLMs) are hard to beat when it comes to instantly parsing reams of publicly available data to generate responses to general knowledge queries. Moreover, challenges around data privacy and recognition of intellectual property often require a level of transparency that simply does not exist in many off-the-shelf models.

Modeling 75

Modeling Insurance Cost-Benefit Unstructured Data 75

DataKitchen

APRIL 13, 2021

Testing and Data Observability. 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 and Data Observability.

Testing 304

Testing Machine Learning Consulting Data Quality 304

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

CIO Business Intelligence

MARCH 25, 2024

Modern digital organisations tend to use an agile approach to delivery, with cross-functional teams, product-based operating models , and persistent funding. But to deliver transformative initiatives, CIOs need to embrace the agile, product-based approach, and that means convincing the CFO to switch to a persistent funding model.

Modeling 124

Modeling Cost-Benefit IT ROI 124

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ). You must detect when the model has become stale, and retrain it as necessary.

Marketing 363

Marketing Experimentation Metrics Testing 363

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

Rocket-Powered Data Science

FEBRUARY 15, 2023

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities. So, if you have 1 trillion data points (g.,

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

DataKitchen

OCTOBER 27, 2022

The first step in solving that pain is to observe what’s happening with your data and analytics ‘estate’ and stick little thermometers at various points in the process and measure. Is the model still predicting accurately? You can’t improve what you can’t measure. Is it late?

Dashboards 130

Dashboards Measurement Testing Modeling 130

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

O'Reilly on Data

OCTOBER 19, 2021

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 352

IT Testing Experimentation Software 352

CIO Business Intelligence

SEPTEMBER 26, 2024

That’s because significant challenges persist in leveraging GenAI’s large language models (LLMs). With these constraints, they must cautiously tread the GenAI line while developing measured strategies for maximizing returns. For instance, organizations can implement ideal code examples and preferred processes into code-writing models.

Enterprise 130

Enterprise Data-driven Risk Testing 130

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 Data Science Sales 93

DataKitchen

NOVEMBER 18, 2022

As he thinks through the various journeys that data take in his company, Jason sees that his dashboard idea would require extracting or testing for events along the way. Data and tool tests. Observability users are then able to see and measure the variance between expectations and reality during and after each run.

Testing 130

Testing Statistics Measurement Metrics 130