Today, Amazon QuickSight announces support for two new question types that simplify and scale complex analytical tasks using natural language. Business users type “forecast” to see future trajectories for up to 3 measures simultaneously. “Why” performs contribution analysis to automatically identify key drivers.

Today, AWS announces the preview of Amazon Elastic Compute Cloud (Amazon EC2) Inf2 instances, which are designed to deliver high performance at the lowest cost in Amazon EC2 for the most demanding deep learning (DL) inference applications. Inf2 instances are powered by up to 12 AWS Inferentia2, the third AWS-designed DL accelerator. Inf2 instances offer 3x higher compute performance, up to 4x higher throughput, and up to 10x lower latency compared to Inf1 instances.

Torn Write Prevention (TWP) is a feature that ensures 16KiB write operations are not torn in the event of operating system crashes or power loss during write transactions. This feature is available for AWS customers using instance store on AWS Nitro SSD based EC2 I4i storage optimized instances, Amazon Elastic Block Store (EBS), a block storage service, when attached to all EC2 Nitro-based instances, and Amazon Relational Database Services (RDS), a fully managed, open-source cloud database.

Today, AWS announces AWS SimSpace Weaver, a new fully managed compute service that helps you deploy large-scale spatial simulations in the cloud. With SimSpace Weaver, you can create seamless virtual worlds with millions of objects that can interact with one another in real time without managing the backend infrastructure.

AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) Hpc6id instances. These instances are optimized to efficiently run memory bandwidth-bound, data-intensive high performance computing (HPC) workloads, such as finite element analysis and seismic reservoir simulations. With EC2 Hpc6id instances, you can lower the cost of your HPC workloads while taking advantage of the elasticity and scalability of AWS.

Contact Lens for Amazon Connect now provides conversational analytics capabilities for Amazon Connect Chat , extending the machine learning powered analytics to better assess chat contacts. These capabilities enable businesses to understand customer sentiment in both agent and chatbot conversations, redact sensitive customer information, and monitor agent compliance with company guidelines to help improve agent performance and customer experience. 

AWS Supply Chain is a cloud-based application that helps supply chain leaders mitigate risks and lower costs to increase supply chain resilience. AWS Supply Chain unifies supply chain data, provides machine learning (ML)–powered actionable insights, and offers built-in contextual collaboration, all of which help you increase customer service levels by reducing stock outs and help you lower costs from overstock.

Amazon Connect agent workspace now provides a step-by-step experience (preview) that guides agents by identifying customer issues and recommending subsequent actions. With Amazon Connect, you can create workflows that walk agents through custom UI pages that suggest what to do at a given moment during a customer interaction. Detailed step-by-step guides increase agent productivity and decrease training time.

Contact Lens for Amazon Connect now provides a set of agent performance evaluation capabilities (preview) that enable contact center managers to create evaluation forms with criteria (e.g, adherence to talk scripts or compliance with sensitive data collection practices) that can be scored using Contact Lens’ machine learning powered conversational analytics. Managers can assess agent performance alongside contact details, recordings, transcripts, and summaries, without the need to switch applications. These capabilities enable managers to assess more agent/customer interactions while reducing the amount of time they spend identifying performance issues and coaching agents to perform their best. 

Amazon Connect forecasting, capacity planning, and scheduling, now generally available, provides new machine learning (ML)–powered capabilities for contact centers. Forecasting, capacity planning, and scheduling help your contact center managers forecast contact demand, determine optimal staffing levels, and ensure the right agents are available at the right time to meet your operational and business goals.