How to get the best price performance in Amazon EC2 for the most demanding machine learning training workloads? Tune in to learn how AWS Trainium-based Amazon EC2 Trn1n instances can help you train your network-intensive generative AI models at scale. Amazon EC2 Trn1n instances double the bandwidth offered by Trn1 instances to 1600 Gbps of EFA and deliver up to 20% faster time-to-train than Trn1 instances. Both Trn1 and Trn1n instances deliver up to 50% savings on training costs over comparable Amazon EC2 instances. Tune in to learn more about this new launch that helps you increase performance, reduce costs, and also improve energy efficiency when training your large-scale ML models. Trn1 Website: https://go.aws/44v90ST AWS Neuron Website: https://bit.ly/46SLyjX AWS Trainium Website: https://bit.ly/3DqiCSM AWS Inferentia Website: https://go.aws/44ymLA9

AWS Glue for Ray makes it easier to scale Python code to process large scale data in AWS Glue. Learn how you can unlock Python at scale for data integration workloads of all sizes, and simplify and streamline your ability to use Ray, a new open source framework for distributing Python across clusters. AWS Glue website: https://go.aws/3pLUTtp AWS Glue Data Integration Engines: https://go.aws/3JYfiSP Blog: AWS Glue for Ray: https://go.aws/43piC04

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. On June 21, 2023 AWS hosted our first Silicon Innovation Day with 20 different deep dives into how these innovations can improve your workflows, impact your business, and power your networking developments. Learn more about everything that was discussed in our roundup podcast and the videos on demand linked in our show notes! All sessions are available here: https://www.youtube.com/@AWSEventsChannel & https://www.youtube.com/playlist?list=PL2yQDdvlhXf9i-4AsyOdlgVFDpBjgwNYw 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

Simon, Hawn, and Jillian take you through over 80 updates!!! Chapters: 01:03 Analytics 03:35 Application Integration 05:22 Compute 10:56 Customer Engagement 11:47 Databases 13:55 Developer Tools 15:38 Front-End Web & Mobile 16:41 Machine Learning 17:15 Management & Governance 21:07 Media Services 21:20 Migration & Transfer 22:32 Networking & Content Delivery 22:56 Partners 24:37 Security, Identity and Compliance 30:26 Storage 32:29 Closing Shownotes: https://d29iemol7wxagg.cloudfront.net/602ExtendedShownotes.html AWS Podcast Audio Feedback: https://bit.ly/3mDc3Y1

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