O'Reilly on Data

JANUARY 21, 2025

Instead of having LLMs make runtime decisions about business logic, use them to help create robust, reusable workflows that can be tested, versioned, and maintained like traditional software. By predefined, tested workflows, we mean creating workflows during the design phase, using AI to assist with ideas and patterns.

Cost-Benefit

DataKitchen

NOVEMBER 5, 2024

Get Off The Blocks Fast: Data Quality In The Bronze Layer Effective Production QA techniques begin with rigorous automated testing at the Bronze layer , where raw data enters the lakehouse environment. Data Drift Checks (does it make sense): Is there a shift in the overall data quality?

Data Quality

DataKitchen

DECEMBER 9, 2024

data quality tests every day to support a cast of analysts and customers. DataKitchen loaded this data and implemented data tests to ensure integrity and data quality via statistical process control (SPC) from day one. The numbers speak for themselves: working towards the launch, an average of 1.5

Data Quality

AWS Big Data

DECEMBER 17, 2024

Conduct data quality tests on anonymized data in compliance with data policies Conduct data quality tests to quickly identify and address data quality issues, maintaining high-quality data at all times. The challenge Data quality tests require performing 1,300 tests on 10 TB of data monthly.

Data Quality

AWS Big Data

OCTOBER 23, 2024

response = client.create( key="test", value="Test value", description="Test description" ) print(response) print("nListing all variables.") variables = client.list() print(variables) print("nGetting the test variable.") Creating a test variable. Creating a test variable. Creating a test variable.

Interactive

KDnuggets

JUNE 12, 2025

When you know hypothesis testing, you know whether your A/B test results actually mean something. Hypothesis testing gives you the framework to make valid and provable claims. Learn t-tests, chi-square tests, and confidence intervals. When you understand distributions, you can spot data quality issues instantly.

Data Science

CIO Business Intelligence

OCTOBER 17, 2024

“This agentic approach to creation and validation is especially useful for people who are already taking a test-driven development approach to writing software,” Davis says. With existing, human-written tests you just loop through generated code, feeding the errors back in, until you get to a success state.”

Software

Analytics Vidhya

JULY 8, 2025

Are you an AI engineer, wondering how to attain resources that can put your skills to a practical test? It might be difficult to look for the right solution for you, based on the vast amount of information out there.

Testing

DataKitchen

JULY 8, 2025

Fix The Fear: Why Data Engineers and Quality Teams Love TestGen We test software code with care and consistency—so why don’t we apply the same discipline to our data? In production, TestGen continuously monitors your data with more than forty column-level tests. Just connect it to your data and start testing.

Scorecard

CIO Business Intelligence

JANUARY 8, 2025

Este tipo de datasets estn especialmente diseados para constituir un test de estrs que ponga al lmite a los modelos. Los investigadores introducen la que constituira una respuesta sesgada para cada situacin, lo que sirve de base para comparar con los resultados que ofrece la IA.

Testing

CIO Business Intelligence

NOVEMBER 19, 2024

Development teams starting small and building up, learning, testing and figuring out the realities from the hype will be the ones to succeed. In our real-world case study, we needed a system that would create test data. This data would be utilized for different types of application testing.

Cost-Benefit

KDnuggets

JUNE 23, 2025

It logs parameters, metrics, and files created during tests. This gives a clear record of what was tested. You can see how each test performed. It saves exact settings used for each test. CI/CD for Machine Learning : Integrate MLflow with Jenkins or GitHub Actions to automate testing and deployment of ML models.

Modeling

KDnuggets

JULY 14, 2025

SciPy: Advanced Statistical Functions and More SciPy builds on NumPy and provides a wide range of advanced statistical functions, probability distributions, and hypothesis testing capabilities. Statsmodels: In-Depth Statistical Modeling Statsmodels is designed for statistical modeling and hypothesis testing. Learn more: [link] 5.

Statistics

O'Reilly on Data

DECEMBER 10, 2024

That seemed like something worth testing outor at least playing around withso when I heard that it very quickly became available in Ollama and wasnt too large to run on a moderately well-equipped laptop, I downloaded QwQ and tried it out. How do you test a reasoning model? But thats hardly a valid test.

Testing

DataFloq

JUNE 10, 2025

A use of such skills would be in hypothesis proving, also known as A/B testing. A/B Testing can determine which of the two pages (A or B) performed better as far as user interaction is concerned. A good example is in determining the effectiveness of a constructed page.

Statistics

KDnuggets

JULY 11, 2025

Quality Evaluation and Testing : Unlike traditional ML models with clear accuracy metrics, evaluating generative AI requires more sophisticated approaches. Design iteratively—test variations and measure results systematically. This requires new approaches to testing, debugging, and quality assurance.

Machine Learning

DataFloq

JULY 28, 2025

Test: Validate timeliness, schema, quality. Engineer: Pipelines, APIs, data contracts, metadata. Deploy & Maintain: Insights monitoring usage, SLA monitoring, logs, support. SLAs, SLOs & Contracts SLAs (Agreements) and SLOs (Objectives) are fundamental for data products.

Data Lake

KDnuggets

JULY 4, 2025

This is the other reason why we previously split the data into training and test data, to have the opportunity to discuss this: in data transformations like standardization of numerical attributes, transformations across the training and test sets must be consistent.

Machine Learning

O'Reilly on Data

MARCH 25, 2025

Weve seen this across dozens of companies, and the teams that break out of this trap all adopt some version of Evaluation-Driven Development (EDD), where testing, monitoring, and evaluation drive every decision from the start. What breaks your app in production isnt always what you tested for in dev! The way out?

Testing

DataFloq

JULY 8, 2025

Bias Auditing and Testing Before feeding data into models, evaluate it for bias, gaps, or systemic issues. Implement fairness metrics and conduct adversarial testing during model training. Maintaining lineage ensures you know the provenance of the data fueling your AI.

Data Quality

AWS Big Data

JANUARY 9, 2025

show(truncate=False) Test results To evaluate the performance and cost benefits of using Iceberg for our quant research data lake, we created four different datasets: two with Iceberg tables and two with direct Amazon S3 Parquet access, each using both sorted and unsorted write distributions. groupBy("exchange_code", "instrument").count().orderBy("count",

Metadata

AWS Big Data

OCTOBER 14, 2024

Launch an EMR cluster with Application Manager placement awareness To perform some tests, you can launch the following AWS CloudFormation stack, which provisions an EMR cluster with managed scaling and the Application Manager placement awareness feature enabled.

Cost-Benefit

CIO Business Intelligence

JANUARY 14, 2025

Its typical for organizations to test out an AI use case, launching a proof of concept and pilot to determine whether theyre placing a good bet. But as CIOs devise their AI strategies, they must ask whether theyre prepared to move a successful AI test into production, Mason says.

Strategy

CIO Business Intelligence

JANUARY 6, 2025

This includes mandating bias testing, diversifying datasets, and holding companies accountable for the societal impacts of their technologies. To ensure it grows responsibly, we need diverse voices at the table developers, policymakers, and community leaders who can represent the needs of all users, not just the privileged few.

IT

AWS Big Data

DECEMBER 20, 2024

We can ask the following question in Amazon Q: update the s3 sink node to write to s3://xxx-testing-in-356769412531/output/ in CSV format in the same way to update the Amazon S3 data target. Upon checking the S3 data target, we can see the S3 path is now a placeholder and the output format is Parquet.

Data Integration

AWS Big Data

DECEMBER 27, 2024

To assess the Spark engines performance with the Iceberg table format, we performed benchmark tests using the 3 TB TPC-DS dataset, version 2.13 (our results derived from the TPC-DS dataset are not directly comparable to the official TPC-DS results due to setup differences). 4xlarge instances, for testing both open source Spark 3.5.3

Cost-Benefit

AWS Big Data

NOVEMBER 27, 2024

Replicate these tests using the older R5 instances as the baseline. FAISS engine results We also examine results from the same tests performed on k-NN indexes configured on the FAISS engine. Using your OpenSearch 2.17 domain, create a k-NN index configured to use either the Lucene or FAISS engine.

Cost-Benefit

AWS Big Data

OCTOBER 11, 2024

Testing and development – You can use snapshots to create copies of your data for testing or development purposes. Migration – Manual snapshots can be useful when you want to migrate data from one domain to another. You can create a snapshot of the source domain and then restore it on the target domain.

Snapshot

AWS Big Data

DECEMBER 5, 2024

To address this, we used the AWS performance testing framework for Apache Kafka to evaluate the theoretical performance limits. We conducted performance and capacity tests on the test MSK clusters that had the same cluster configurations as our development and production clusters.

Metrics

DataKitchen

MAY 17, 2025

An embedded test had failed. And I was tempted, so tempted, as the clock kept ticking, to disable the test and let it go. Then it dawned on me that this test wasnt even ours. These tests werent easy to define or implement. We trusted stakeholders to define critical business rules that would test for major problems.

Data Quality