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

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 300

Testing Measurement Consulting Modeling 300

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 300

Testing Machine Learning Consulting Data Quality 300

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 362

Marketing Experimentation Metrics Testing 362

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 169

Testing Data Quality Predictive Modeling Metrics 169

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

OCTOBER 28, 2021

Machine learning (ML) models are computer programs that draw inferences from data — usually lots of data. ML is part of artificial intelligence (AI), which is a broader term used to describe computers and software that perform tasks in a way that we humans consider “intelligent.” What Is AI Bias?

Testing 130

Testing IT Manufacturing Metrics 130

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 119

Modeling Cost-Benefit IT ROI 119

CIO Business Intelligence

APRIL 17, 2024

They had ChatGPT write the script, and other gen AI tools to create a digital person who reads the script, a scalable process with at least one measurable benefit: speed. Helping software developers write and test code Similarly in tech, companies are currently open about some of their use cases, but protective of others.

Enterprise 116

Enterprise Testing Digital Transformation Software 116

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

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.

Measurement 246

Measurement Metrics Data-driven Testing 246

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 91

Modeling Statistics Machine Learning Testing 91

DataKitchen

NOVEMBER 29, 2021

From our unique vantage point in the evolution toward DataOps automation, we publish an annual prediction of trends that most deeply impact the DataOps enterprise software industry as a whole. Model developers will test for AI bias as part of their pre-deployment testing. AI Accountability. Companies Commit to Remote.

Testing 245

Testing Data Lake Data Architecture Manufacturing 245

Smart Data Collective

MARCH 28, 2022

It can be further classified as statistical and predictive modeling, but the two are closely associated with each other. They can be again classified as random testing and optimization. This includes studying factors like test scores, teacher performances, and graduation rates.

Data Analytics 85

Data Analytics Software Analytics Statistics 85

CIO Business Intelligence

MARCH 3, 2024

Worse, it said “adversaries are accelerating and widening their range of sophisticated attacks at scale through automation, cybercrime-as-a-service models, impersonations and adaptation.” Doing penetration testing. Fortunately, there are tools available to deal with the specific security challenges presented by printers.

IT 102

IT Measurement Risk Testing 102

CIO Business Intelligence

MARCH 5, 2024

India’s Ministry of Electronics and Information Technology (MeitY) has caused consternation with its stern reminder to makers and users of large language models (LLMs) of their obligations under the country’s IT Act, after Google’s Gemini model was prompted to make derogatory remarks about Indian Prime Minister Narendra Modi.

Testing 124

Testing Modeling Cost-Benefit Reporting 124

DataKitchen

MAY 10, 2021

In modern IT and software dev, people use the term observability to include the ability to find the root cause of a problem. Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. Manual testing is performed step-by-step, by a person.

Testing 214

Testing Manufacturing Data Quality Statistics 214

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

IBM Big Data Hub

JANUARY 16, 2024

Today, as many as half of all companies—and 70% of top-performing companies— use software developed internally to differentiate themselves in crowded markets. As more businesses become software businesses, a long-term development strategy that prioritizes continuous feedback and core organizational value is key to success.

Strategy 83

Strategy Cost-Benefit Testing Manufacturing 83

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?

Metrics 141

Metrics KPI Measurement Dashboards 141

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 359

Risk Modeling Data Processing Software 359

Smart Data Collective

AUGUST 30, 2022

With cloud computing, companies can use their servers and hardware as much as they want with little overhead from extra hardware and software. The model enables easy transfer of cloud services between different geographic regions, either onshore or offshore. Testing new programs. Multi-cloud computing. Centralized data storage.

Technology 134

Technology Enterprise Cost-Benefit Testing 134

datapine

APRIL 15, 2020

Your Chance: Want to test an agile business intelligence solution? Try our business intelligence software for 14 days, completely free! The term “agile” was originally conceived in 2011 as a software development methodology. Working software over comprehensive documentation. What Is Agile Analytics And BI?

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

CIO Business Intelligence

MAY 14, 2024

Enterprises need to ensure that private corporate data does not find itself inside a public AI model,” McCarthy says. You don’t want a mistake to happen and have it end up ingested or part of someone else’s model. The excitement and related fears surrounding AI only reinforces the need for private clouds.

IT 136

IT Data Processing Enterprise Modeling 136

AWS Big Data

MARCH 3, 2023

Tricentis is the global leader in continuous testing for DevOps, cloud, and enterprise applications. Speed changes everything, and continuous testing across the entire CI/CD lifecycle is the key. However, speed is only realized when you have the confidence to release software on demand.

Software 52

Software Data Lake Testing Cost-Benefit 52

DataKitchen

JANUARY 20, 2021

Centralizing analytics helps the organization standardize enterprise-wide measurements and metrics. A centralized team can publish a set of software services that support the rollout of Agile/DataOps. Develop/execute regression testing . Test data management and other functions provided ‘as a service’ .

Metrics 243

Metrics Experimentation Measurement Testing 243

Timo Elliott

DECEMBER 11, 2023

At dinner the other day, my daughter was explaining a complex probability question in a recent mathematics test. My wife asked if she had the test paper, and suggested she put into ChatGPT to get help. But that’s what’s so confounding about the latest large language models. I’m sure everybody can relate with that idea.

Testing 69

Testing Measurement Modeling Software 69

CIO Business Intelligence

OCTOBER 27, 2023

Each Hangar houses agile coaches, business analysts, data and analytics specialists, product owners, Scrum masters, software engineers, and user interface designers — all with one mission: to elevate the airlines’ digital customer experience before, during, and after the flight.

Digital Transformation 117

Digital Transformation B2B Measurement Modeling 117

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. Data Journeys run on software, on servers, and with code.

Testing 130

Testing Cost-Benefit Metrics Data Quality 130

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

Smart Data Collective

JUNE 7, 2019

However, recent advances in applying transfer learning to NLP allows us to train a custom language model in a matter of minutes on a modest GPU, using relatively small datasets,” writes author Euan Wielewski. Appreciating the Machine Learning Technology Behind Modern Task Management Software. Final Thoughts. Happy learning!

Machine Learning 78

Machine Learning Software Management Data-driven 78

Smart Data Collective

SEPTEMBER 13, 2023

For instance, used Dell servers , which are refurbished, offer the same performance as Dell servers of the same make and model; however, they come for a fraction of the cost. To ensure robust protection and resilience against such threats, organizations and individuals must go beyond installing security tools and software.

IT 101

IT Cost-Benefit Data Processing Software 101

IBM Big Data Hub

DECEMBER 19, 2023

This process, often referred to as the “pathway to deploy,” encompasses a series of intricate steps and decisions that can significantly impact an organization’s ability to deliver software efficiently, reliably and at scale. We recommend a 4-stage roadmap for implementation, as shown in the figure below.

Testing 82

Testing Enterprise Modeling Software 82

CIO Business Intelligence

MARCH 13, 2024

We did side-by-side testing,” he says. So based on early results, the three functional areas where companies see the biggest productivity improvements are in customer service, software development, and general creative and knowledge work. Much of this improvement was concentrated in junior staff, he adds. “AI

IT 126

IT Consulting Testing Cost-Benefit 126

CIO Business Intelligence

MARCH 18, 2024

Developers of risky generative AI models are now free to release them without government approval, but are being encouraged to self-regulate by adding labels to the output of their models warning of their potential unreliability. Akshara Bassi, senior analyst at Counterpoint, weighed in on the implications for AI model developers.

Metadata 73

Metadata Visualization Modeling Risk 73

DataKitchen

OCTOBER 5, 2021

Clear measurement and monitoring of results. The data scientist’s products are models and segmentations. Instead of focusing on a narrowly defined task with minimal testing and feedback, DataOps focuses on adding value. Create tests. Measure success. Low error rates. The data engineer builds data transformations.

Testing 152

Testing Dashboards Measurement Experimentation 152

CIO Business Intelligence

MARCH 27, 2024

Organizations can also start testing the algorithms in their environments and check their supply chain partners are doing the same. The types of models we run in the bank every day are closely matched to the types of models a quantum computer can consume.”

Optimization 137

Optimization Consulting Machine Learning Testing 137