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

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 103

Forecasting Metrics Testing Modeling 103

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 284

Metrics Reporting Measurement Finance 284

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. 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.

Marketing 362

Marketing Experimentation Metrics Testing 362

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 112

Digital Transformation Modeling Management Data-driven 112

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

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

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. Moreover, advanced metrics like Percentage Regional Sales Growth can provide nuanced insights into business performance.

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

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 123

Modeling Cost-Benefit IT ROI 123

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 131

Metrics Analytics Cost-Benefit Sales 131

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

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

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

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 89

Modeling Statistics Machine Learning Testing 89

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

DataKitchen

APRIL 26, 2021

GSK had been pursuing DataOps capabilities such as automation, containerization, automated testing and monitoring, and reusability, for several years. Furthermore, the introduction of AI and ML models hastened the need to be more efficient and effective in deploying new technologies. Multiple Metrics for Success.

Measurement 246

Measurement Metrics Data-driven Testing 246

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 307

Testing Machine Learning Consulting Data Quality 307

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

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 80

Risk Modeling Risk Management Measurement 80

DataKitchen

JANUARY 20, 2021

Centralizing analytics helps the organization standardize enterprise-wide measurements and metrics. With a standard metric supported by a centralized technical team, the organization maintains consistency in analytics. Develop/execute regression testing . Test data management and other functions provided ‘as a service’ .

Metrics 243

Metrics Experimentation Measurement Testing 243

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. First and foremost, we must reconsider our approach to initiatives, moving away from a solely awareness-focused, compliance-driven model.

Metrics 94

Metrics Measurement Risk Data Processing 94

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

Occam's Razor

APRIL 8, 2013

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. This should not be news to you. But it is not routine.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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. In addition to the tracking of relationships and quality metrics, DataOps Observability journeys allow users to establish baselines?concrete

Testing 130

Testing Statistics Measurement Metrics 130

DataKitchen

NOVEMBER 29, 2021

Model developers will test for AI bias as part of their pre-deployment testing. Quality test suites will enforce “equity,” like any other performance metric. Continuous testing, monitoring and observability will prevent biased models from deploying or continuing to operate. Companies Commit to Remote.

Testing 245

Testing Data Lake Data Architecture Manufacturing 245

DataKitchen

JUNE 19, 2023

Our customers start looking at the data in dashboards and models and then find many issues. It involves tracking key metrics such as system health indicators, performance measures, and error rates and closely scrutinizing system logs to identify anomalies or errors. The image above shows an example ‘’data at rest’ test result.

Testing 130

Testing Cost-Benefit Metrics Data Quality 130

datapine

JULY 26, 2022

Enter small business dashboards and metrics. Your Chance: Want to test a small business dashboard software? What Are Small Business Metrics? Small business metrics are performance measurements that provide insights into the progress of different activities and company goals. How you might be wondering?

Dashboards 151

Dashboards KPI Metrics Sales 151

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 85

Management IT Machine Learning Metrics 85

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 87

Metrics Machine Learning Risk Statistics 87

IBM Big Data Hub

JANUARY 16, 2024

Typically, these measures should be undertaken by a dedicated development team or through a product development partnership with an experienced and specialized consultancy. Depending on the product, this phase may also involve wire-framing and modeling as well as costing the price of materials or server space.

Strategy 81

Strategy Cost-Benefit Testing Manufacturing 81

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

Rocket-Powered Data Science

JULY 21, 2023

Here are four specific metrics from the report, highlighting the potentially huge enterprise system benefits coming from implementing Splunk’s observability and monitoring products and services: Four times as many leaders who implement observability strategies resolve unplanned downtime in just minutes, not hours or days.

Machine Learning 189

Machine Learning Metrics Reporting Internet of Things 189

datapine

JUNE 18, 2019

A data-driven finance report is also an effective means of remaining updated with any significant progress or changes in the status of your finances, and help you measure your financial results, cash flow, and financial position. Work Quality: These metrics help companies determine the quality level of their employees’ work performance.

Reporting 251

Reporting Metrics KPI Finance 251

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

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

DataKitchen

OCTOBER 11, 2021

There are no automated tests , so errors frequently pass through the pipeline. There is no process to spin up an isolated dev environment to quickly add a feature, test it with actual data and deploy it to production. Finally, when your implementation is complete, you can track and measure your process.

Testing 157

Testing Metadata Dashboards Statistics 157

AWS Big Data

MARCH 11, 2024

Managed Service for Apache Flink manages the underlying infrastructure and Apache Flink components that provide durable application state, metrics, logs, and more. However, it’s crucial to recognize that this reduction could adversely affect performance if not thoroughly assessed and tested.

Management 93

Management Snapshot Metrics Cost-Benefit 93