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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.
This report details our findings about how well Clarifai scored against 10 criteria and where they stand in relation to each other. AD&D professionals can use this review to select the right partner for their computer vision needs.
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.
In the Forrester 27-criterion evaluation of people-focused AI-based text analytics platforms, IBM was identified as one of the significant providers.
Hazelcast Cloud<sup>®</sup> is an on-demand managed service for Hazelcast IMDG (InMemory Data Grid). It offers a scalable, shared pool of RAM across multiple dedicated cloud instances to provide low-latency access to your data in a secure, always-on environment. It is maintained by Hazelcast to free you from day-to-day infrastructure management responsibilities. Hazelcast Cloud is available on the Hazelcast website on an annual contract basis or pay-as-you-go.
Hazelcast IMDG® is a cloud-native, distributed, in-memory computing platform that helps companies manage their data by using in-memory storage and performing parallel execution for breakthrough application speed and scale. IMDG is the leading open source in-memory data grid on the market with millions of deployed systems worldwide. IMDG is also available in commercial versions ("Enterprise" and "Pro") that add more enterprise-ready capabilities.
Hazelcast Jet® is a stream processing engine designed for high performance, easy maintenance, and resilience. Its architecture entails a distributed engine that leverages in-memory computing to run applications at near real-time speeds. Jet is used to develop both stream and batch processing applications using a directed acyclic graph (DAG) for job planning to optimally leverage resources when parallelizing subtasks.