Category: Amazon Machine Learning

It’s critical to identify and protect the sensitive data collected from any unauthorized disclosure, and it’s the responsibility of every organization to effectively discover, control, and manage their sensitive data footprints and comply with relevant data protection laws. Learn how HCLTech‘s DataPatrol framework accomplishes critical tasks in the lifecycle of sensitive documents and improves sensitive data discovery and governance across your AWS environment.

Organizations have matured and have overcome the initial hurdles of proving the capabilities of AI. The challenge now is operationalizing AI and building engineering excellence to successfully adopt and manage machine learning at scale. Learn how Quantiphi assisted Venterra Realty in bringing in the best ML solution development and deployment practices through NeuralOps—a framework built on Amazon SageMaker.

With Amazon Comprehend, the implementation of natural language processing and machine learning has become a simple, routine task. Organizations no longer have to spend hours trying to pick the right algorithm, as Amazon Comprehend automatically selects the best ones for any given use case. To explore this benefit, learn about an implementation of Amazon Comprehend for risk prediction and sentiment analysis on the observations in clinical trials sites.

In computer vision applications, the transmission of video data to the cloud for analysis can result in added delays due to various contributing factors such as queuing, propagation, and network latency. Learn how the Nordcloud team, in collaboration with AWS, has designed a “Computer Vision at the Edge” solution based on AWS Panorama. It caters to organizations seeking low-latency decision making without the burden of managing complex technology.

Metal Toad has been working with major entertainment brands for decades, including keeping some of the highest-profile media sites live under unique traffic conditions. Keeping these sites up and running is one of Metal Toad’s superpowers, but the AWS Digital Customer Experience Competency Partner couldn’t do it without the tools provided by AWS. Explore some of the strategies Metal Toad deployed to protect a customer’s site during an event where failure was not an option.

A small number of companies manage to leverage the true value of their machine learning proofs of concept, and the majority of those are still struggling to overcome the experiment-production gap for their AI applications fueled by machine learning and data. Learn about MLOps, why organizations should care about it on their AI journey, and how DXC Technology and AWS can help to quickly integrate MLOps best practices into your daily business using the MLOps Quick Start for MLOps on AWS.

Learn how equipment operators can build a predictive maintenance solution using AutoML and no-code tools powered by AWS. This type of solution delivers significant gains to large-scale industrial systems and mission-critical applications where the costs associated with machine failure or unplanned downtime can be high. The design of this solution is based on the experience of Grid Dynamics with manufacturing clients.

Through the Pega Platform and Amazon SageMaker, you can easily streamline the development and operationalization of machine learning models to improve process automation. This allows customers to combine the strengths of cloud, data, and machine learning with AI-powered decisioning and smart workflow capabilities. It also enables customers to operationalize and monetize data and insight, drive process efficiency and effectiveness, and improve customer experience and value.

Conversational AI solutions, like chatbots and interactive voice response systems (IVR), are a key component of enterprises’ customer service strategy. AWS recently ran a survey, through ESG, on consumers’ opinions of automated customer service solutions like chatbots and IVRs. Conversational AI solutions have come a long way from basic FAQ experiences, and while we see strong positive signals of consumer interest in automated solutions, there are still areas for improvement.

Federated learning (FL) addresses the need of preserving privacy while having access to large datasets for machine learning model training. The NVIDIA FLARE (which stands for Federated Learning Application Runtime Environment) platform provides an open-source Python SDK for collaborative computation and offers privacy-preserving FL workflows at scale. NVIDIA is an AWS Competency Partner that has pioneered accelerated computing to tackle challenges in AI and computer graphics.