This whitepaper describes four key practices for database teams to adopt when evolving their database development process to follow a DevOps approach. For teams who are already following a DevOps approach it serves as a way to check that best practices are being following.
This whitepaper outlines how organizations can introduce compliant database DevOps by transforming some of the processes involved in four key areas: Standardized team-based development, Automated deployments, Performance and availability monitoring, Protecting and preserving data.
This whitepaper reveals the benefits, and demonstrates how their appeal changes when viewed from the perspective of a CEO, a CIO, or an IT manager.
A comprehensive list of essential network security controls mapped to NIST requirements.
Join DLT & Red Hat as they walk through the components and benefits of adapting Simple Content Access (SCA) into your administrative process.
This paper provides practical guidance for CISOs, CIOs, and DevOps leaders for designing an effective application security program to secure modern application development via an integrated approach. The paper also aims to equip application security practitioners with research data to support building the business case for AST investments.
The 12 Providers That Matter Most and How They Stack Up. This report shows how each provider measures up and helps security and risk professionals select the right one for their needs.
DLT’s Secure Software Factory (SSF) supports modernization efforts to transform agencies from maintainers of brittle, legacy applications to producers of flexible, secure, and portable workloads across a hybrid cloud environment.
As the proliferation of software continues, bringing with it an ever-expanding attack surface that’s ripe for targeting by malicious actors, securing software must be a priority above all else. It’s time to turn the tides. It’s time to turn complacency into proactivity. Here are five reasons why it’s time to prioritize software security.
Welcome to another episode of the DLT ContinuousX podcast! Today we will be discussing DevOps (and more) with Ryan O'Daniel - Senior Federal Systems Engineer at Sysdig.
High quality data is the most important factor in building machine learning models. The majority of the time, training data presents problems stemming from how the data was produced and labeled internally. Other times, AI projects stall because of the lack of internal resources and skills to label data.
Data labeling is the first step in the Al lifecycle. It is the foundation to developing accurate, predictive models that power a myriad of outcomes. Simply, data labeling is the act of adding keywords to unstructured data, like video, images, audio, or text, so that machines learn to automatically recognize the concepts that these keywords describe.
Search is at the heart of modern e-commerce. The tasks of ranking search results, product personalization and product discovery are critical to driving revenue. AI allows retailers to better capture and leverage the available data that powers personalized experiences.
As Artificial Intelligence's impact on the world continues to grow, more and more business stakeholders will encounter and grapple with the lexicon.
The machine learning techniques that ignited breakthroughs in computer vision are now being applied to language with powerful results.