In the fast-paced world of machine learning, innovation requires utilizing data. However the reality for many companies is that data access and environmental controls which are vital to security can also add inefficiencies to the model development and testing life cycle. To overcome this challenge — and help others with it as well — Capital … continue reading
As we venture deeper into the realm of machine learning and Generative AI (GenAI), the emphasis on data quality becomes paramount. John Jeske, CTO for the Advanced Technology Innovation Group at KMS Technology, delves into data governance methodologies such as data lineage tracing and federated learning to ensure top-tier model performance. “Data quality is the … continue reading
One of the biggest challenges faced by companies who work with large amounts of data is that their databases may end up with several instances of duplicate records, leading to an inaccurate overall picture of their customers. According to Tim Sidor, data quality analyst at Melissa, there are a number of reasons why duplicate records … continue reading
Companies in certain industries – banking, healthcare, and the like – are subject to many different regulations when it comes to things like how they store user data, required communications with customers, and what data can and can’t be collected. For example, financial companies need to comply with Anti-Money Laundering (AML) and Combating the Financing … continue reading
Studies show that DevOps adoption is still a moving target for the vast majority of software development teams, with just 11% reporting full DevOps maturity in 2022. Navigating this transition requires organization-wide metrics that help everyone understand their role. To that end, Google developed the DORA (DevOps Research and Assessment) metrics to give development teams … continue reading
Many companies need to be able to verify the identity of their customers for a variety of reasons, but for some industries this isn’t just a best practice, but rather a necessity in order to comply with regulations. In the financial industry, for example, companies should have programs in place to meet Anti-Money Laundering (AML) … continue reading
In the current age of digital transformation, data has emerged as the cornerstone of business operations. The rapid accumulation of information brings opportunities and challenges for organizations seeking to harness data’s potential. As evidenced by recent statistics from the Reveal survey, data-driven strategies are on the rise, with eight in ten software developers (80.8%) incorporating … continue reading
Test automation has undergone quite an evolution in the decades since it first became possible. Yet despite the obvious benefits, the digitalization of the software development industry has created some new challenges. It comes down to three big things, according to Kevin Parker, vice president of product at Appvance. The first is velocity and how … continue reading
The following is a listing of automated testing tool providers, along with a brief description of their offerings. FEATURED PROVIDERS APPVANCE is the leader in generative AI for Software Quality. Its premier product AIQ is an AI-native, unified software quality platform that delivers unprecedented levels of productivity to accelerate digital transformation in the enterprise. Leveraging generative … continue reading
How does a quality organization run? And how does it deliver a quality product for consumers? According to Roya Montazeri, senior director of test and quality at Cox Automotive, no one tool or approach can solve the quality problem. Cox Automotive portfolios, she said, is a specialized software company that addresses the buying, selling, trading … continue reading
One area in which test automation can deliver big value to organizations is in accessibility. Accessibility is all about the user experience, and is especially important for users with disabilities. Automated end-to-end testing helps answer the question of how easy or difficult it is for users to engage with the software. “If the software is … continue reading
Code coverage and end-to-end testing – sometimes called path testing – are particularly well-suited for automation, but they’re only as good as the training and implementation. Since AI doesn’t have an imagination, it is up to the model and whoever is feeding in that data to cover as many paths as you can in an … continue reading
AI is the talk of the town and it seems like every software provider would like to have AI-powered features in their software. But in order to do that, you need AI models that you can train. One of the newer approaches to model training in machine learning is federated learning (FL), which is an … continue reading
Data can be an organization’s most valuable tool, but not if your database is full of people named ‘Mickey Mouse’ or has out-of-date addresses. According to Michael Lee, solution engineer at data verification solution provider Melissa, the most common issues that could be present in your database tend to be the simple ones, such as … continue reading
