Tag: Deep Learning
Forward-looking finance teams will build resilience and responsiveness to future disruption. To deliver this strategic mandate, finance offices must be data-driven and share insights that help the organization connect, predict, and adapt within a changing business environment. Learn how Genpact’s data lake analytics reference architecture helps finance teams incorporate functional expertise into AWS solutions with the goal of delivering business impact beyond increased productivity.
Many organizations have started applying machine learning and artificial intelligence expertise to scale customer communications and accelerate research during the COVID-19 pandemic. Onica has been actively involved in these efforts, leveraging AWS technologies to help decision makers navigate this pandemic. In this post, dive into the technical details of two COVID-19-related solutions Onica has produced and learn about their results and impact.
VPC traffic mirroring and VPC ingress routing are powerful AWS networking primitives to monitor network traffic in your VPC at the packet-level. With Blue Hexagon’s next-gen Network Detection and Response (NG-NDR) security tool for AWS, which is powered by real-time deep learning, you can detect threats in network headers and payloads in less than a second. The additional AWS Security Hub integration enables you to trigger a rich action space of remediation and response.
Inference is an important stage of machine learning pipelines that deliver insights to end users from trained neural network models. These models are deployed to perform predictive tasks like image classification, object detection, and semantic segmentation. However, constraints can make implementing inference at scale on edge devices such as IoT controllers and gateways challenging. Learn how to train and convert a neural network model for image classification to an edge-optimized binary for Intel FPGA hardware.
Deep learning is inspired by the human brain and once a brain learns to identify an object, its identification becomes second nature. Similarly, as Deep Instinct’s artificial neural network learns to detect more and more types of cyber threats, its prediction capabilities become instinctive. As a result, malware both known and new can be predicted and prevented in zero-time. Deep Instinct’s predictive threat prevention platform can be applied against known or unknown threats, whether it be a file or fileless attack.