AWS Podcast

Responsible for secure access to your corporate applications? Hear from Product Management Lead Shovan Das as he discusses a new simplified and secure remote connectivity option on AWS. Built on Zero Trust guiding principles, AWS Verified Access is a service that can be used to validate every application request before granting access. Verified Access removes the need for a VPN, which simplifies the remote connectivity experience for end users and reduces the management complexity for IT administrators. Learn more by visiting the AWS Verified Access website: https://go.aws/3XCKlZO Please take two minutes to fill out our survey regarding all the changes to the Official AWS Podcast: https://bit.ly/43hJmzs

Amazon SageMaker Multi-Model Endpoint (MME) is fully managed capability of SageMaker Inference that allows customers to deploy thousands of models on a single endpoint and save costs by sharing instances on which the endpoints run across all the models. Until recently, MME was only supported for machine learning (ML) models which run on CPU instances. Now, customers can use MME to deploy thousands of ML models on GPU based instances as well, and potentially save costs by 90%. MME dynamically loads and unloads models from GPU memory based on incoming traffic to the endpoint. Customers save cost with MME as the GPU instances are shared by thousands of models. Customers can run ML models from multiple ML frameworks including PyTorch, TensorFlow, XGBoost, and ONNX. Customers can get started by using the NVIDIA Triton™ Inference Server and deploy models on SageMaker’s GPU instances in “multi-model“ mode. Once the MME is created, customers specify the ML model from which they want to obtain inference while invoking the endpoint. Multi Model Endpoints for GPU is available in all AWS regions where Amazon SageMaker is available. To learn more checkout: Our launch blog: https://go.aws/3NwtJyh Amazon SageMaker website: https://go.aws/44uCdNr

How can you build designs faster and predict the weather more efficiently? How can you can carry out complex calculations across HPC clusters using up to tens of thousands of cores with high performance and lower costs? In this podcast, Heidi Poxon, Principal HPC Technologist, talks about using high performance computing (HPC) for a range of use cases from building designs for aircraft and race cars to climate modeling. You’ll learn about newly launched Amazon Elastic Compute Cloud (Amazon EC2) Hpc7g instances, powered by AWS Graviton processors, which are custom Arm-based processors designed by Amazon Web Services (AWS). With these instances you can scale your HPC clusters to run compute-intensive workloads such as Computational Fluid Dynamics (CFD), weather forecasting, and molecular dynamics. Amazon EC2 Hpc7g Instances website: https://go.aws/3NPhFtk Amazon EC2 Hpc7g Instances Blog: https://go.aws/3raBXVc

A rapid-fire set up updates with a few Deep Dives for good measure! Chapters: 01:18 AWS Marketplace 01:38 Analytics 03:44 Application Integration 04:28 Compute 05:45 Databases 06:45 Internet of Things (IoT) 06:59 Machine Learning 08:13 Management & Governance 10:50 Media Services 12:19 Migration & Transfer 13:45 Security, Identity and Compliance Shownotes: https://d29iemol7wxagg.cloudfront.net/598ExtendedShownotes.html AWS Podcast Audio Feedback: https://bit.ly/3mDc3Y1

Amazon Connect provides a unified agent workspace that is easy and less stressful for new agents to learn and gives experienced agents all of the tools they need to continuously improve their customer experience. In this podcast episode, learn how you can create step-by-step guides that recommend and display next best actions for agents to resolve customer issues faster. Learn more about Amazon Connect: https://go.aws/3CGXqHT and Amazon Connect Agent Workspace here: https://bit.ly/3qZPYVG

Silicon chips are the foundation of modern computing. AWS custom-designs its silicon chips to be more efficient and sustainable, which helps you maximize performance and save money. To learn how AWS custom-designed silicon chips can help your business, tune in to AWS Silicon Innovation Day on June 21, at 9 a.m. Pacific Time at twitch.tv/awsonair or LinkedIn. Register at Splash That: https://bit.ly/3PkzMZd Learn more about Graviton: https://go.aws/42MJ4Ao Learn more about Inferentia: https://go.aws/3N7pM2M Learn more about Trainium: https://go.aws/3N7q0qE Learn more about Nitro: https://go.aws/3CDpCM2

Amazon Security Lake is a new service that centralizes security data from AWS environments, SaaS providers, on premises, and cloud sources into a purpose-built data lake stored in the customer’s account. With Security Lake, customers can gain a more complete understanding of security data across their entire organization. In this episode, Himanshu Verma, Worldwide Specialist for AWS Security Services, joins Julian Ford to discuss the customer problems that Amazon Security Lake solves and provide guidance on getting started with Security Lake. Amazon Security Lake website: https://go.aws/3JipANj Blog: Amazon Security Lake is now generally available: https://go.aws/440F0xE Blog: Amazon Security Lake and Azure activity logs: https://go.aws/3XektmT

Simon, Hawn, and Jillian cover all the updates you need to know about! Chapters: 00:52 AWS Marketplace 01:21 Analytics 03:23 Application Integration 05:38 Compute 07:06 Customer Engagement 10:27 Databases 15:42 Developer Tools 17:03 Front-End Web & Mobile 18:46 GameTech 19:29 Machine Learning 23:32 Management & Governance 26:34 Media Services 27:43 Migration & Transfer 29:40 Networking & Content Delivery 32:34 Security, Identity and Compliance 35:11 Storage Shownotes: https://d29iemol7wxagg.cloudfront.net/594ExtendedShownotes.html AWS Podcast Audio Feedback: https://bit.ly/3mDc3Y1

Hundreds of thousands of customers build data lakes everyday and these data lakes can quickly become data swamps if they dont pay attention to the data quality. Setting up data quality can be a time-consuming, tedious process. Shiv Narayanan, Product Manager for AWS Glue chats with (Name) about the launch of AWS Glue Data Quality. AWS Glue Data Quality Quality builds confidence in your data by ensuring high data quality. It automatically measures, monitors, and manages data quality in your data lakes and data pipelines, so it’s easy to identify missing, stale, or bad data. Take the steps to make your data lake crystal clear. Whats New: AWS Glue Data Quality: https://go.aws/3IWPXIw AWS Glue Data Quality: https://go.aws/3MUrk07 Blog: AWS Glue Data Quality: https://go.aws/42nq6jG

Speed matters when processing large data sets. Media & entertainment (rendering and transcoding), healthcare & life sciences (genomics and microscopy), and other compute intensive workloads use cloud bursting to augment internal compute resources with the goal of completing projects faster. Amazon File Cache is a recently introduced high speed cache which brings the power and scale of AWS to your on-premises workloads. Tune in to Jordan Dolman, Sr. Product Manager to learn how Amazon File Cache enables you to complete your projects faster and optimize compute resources. Getting started with Amazon File Cache: https://go.aws/3qsVakB