AWS Podcast

Is your company faced with inaccurate and fragmented data, scored across applications, channels, and data stores? Find out how AWS Entity Resolution helps you match and link related records with configurable workflows that take only minutes to set up. Tune into listen to Shobhit Gupta, Principal Product Manager, Technical, talk about how this new new service helps you easily set up matching workflows and configure advanced matching techniques, while helping you better protect your data.

Simon takes you on a tour of your GenAI options. From software development, to AI policy, to trialling FMs, to new instance types, and CPUs. Referenced Links: - URL: https://aws.amazon.com/blogs/devops/optimize-software-development-with-amazon-codewhisperer/ Title: Optimize software development with Amazon CodeWhisperer | AWS DevOps Blog - URL: https://aws.amazon.com/blogs/devops/10-ways-to-build-applications-faster-with-amazon-codewhisperer/ Title: 10 ways to build applications faster with Amazon CodeWhisperer | AWS DevOps Blog - URL: https://aws.amazon.com/blogs/big-data/build-data-integration-jobs-with-ai-companion-on-aws-glue-studio-notebook-powered-by-amazon-codewhisperer/ Title: Build data integration jobs with AI companion on AWS Glue Studio Notebook powered by Amazon CodeWhisperer - URL: https://aws.amazon.com/blogs/machine-learning/use-generative-ai-foundation-models-in-vpc-mode-with-no-internet-connectivity-using-amazon-sagemaker-jumpstart/ Title: Use generative AI foundation models in VPC mode with no internet connectivity using Amazon SageMaker JumpStart | AWS Machine Learning Blog - URL: https://aws.amazon.com/blogs/machine-learning/llama-2-foundation-models-from-meta-are-now-available-in-amazon-sagemaker-jumpstart/ Title: Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart | AWS Machine Learning Blog - URL: https://aws.amazon.com/blogs/machine-learning/use-stable-diffusion-xl-with-amazon-sagemaker-jumpstart-in-amazon-sagemaker-studio/ Title: Use Stable Diffusion XL with Amazon SageMaker JumpStart in Amazon SageMaker Studio | AWS Machine Learning Blog - URL: https://aws.amazon.com/blogs/machine-learning/efficiently-train-tune-and-deploy-custom-ensembles-using-amazon-sagemaker/ Title: Efficiently train, tune, and deploy custom ensembles using Amazon SageMaker | AWS Machine Learning Blog - URL: https://aws.amazon.com/blogs/machine-learning/quickly-build-high-accuracy-generative-ai-applications-on-enterprise-data-using-amazon-kendra-langchain-and-large-language-models/ Title: Quickly build high-accuracy Generative AI applications on enterprise data using Amazon Kendra, LangChain, and large language models | AWS Machine Learning Blog - URL: https://aws.amazon.com/bedrock/features/ Title: Foundation Model API Service – Amazon Bedrock Features – AWS - URL: https://aws.amazon.com/blogs/aws/preview-enable-foundation-models-to-complete-tasks-with-agents-for-amazon-bedrock/ Title: Preview – Enable Foundation Models to Complete Tasks With Agents for Amazon Bedrock | AWS News Blog - URL: https://aws.amazon.com/blogs/business-intelligence/announcing-generative-bi-capabilities-in-amazon-quicksight/ Title: Announcing Generative BI capabilities in Amazon QuickSight | AWS Business Intelligence Blog - URL: https://aws.amazon.com/about-aws/whats-new/2023/05/amazon-rds-postgresql-pgvector-ml-model-integration/ Title: Amazon RDS for PostgreSQL now supports pgvector for simplified ML model integration - URL: https://aws.amazon.com/blogs/big-data/introducing-the-vector-engine-for-amazon-opensearch-serverless-now-in-preview/ Title: Introducing the vector engine for Amazon OpenSearch Serverless, now in preview | AWS Big Data Blog - URL: https://aws.amazon.com/blogs/aws/new-amazon-ec2-p5-instances-powered-by-nvidia-h100-tensor-core-gpus-for-accelerating-generative-ai-and-hpc-applications/ Title: New – Amazon EC2 P5 Instances Powered by NVIDIA H100 Tensor Core GPUs for Accelerating Generative AI and HPC Applications | AWS News Blog - URL: https://aws.amazon.com/blogs/machine-learning/maximize-stable-diffusion-performance-and-lower-inference-costs-with-aws-inferentia2/ Title: Maximize Stable Diffusion performance and lower inference costs with AWS Inferentia2 | AWS Machine Learning Blog - URL: https://aws.amazon.com/blogs/machine-learning/new-technical-deep-dive-course-generative-ai-foundations-on-aws/ Title: New technical deep dive course: Generative AI Foundations on AWS | AWS Machine Learning Blog - URL: https://aws.amazon.com/blogs/machine-learning/aws-offers-new-artificial-intelligence-machine-learning-and-generative-ai-guides-to-plan-your-ai-strategy/ Title: AWS offers new artificial intelligence, machine learning, and generative AI guides to plan your AI strategy | AWS Machine Learning Blog - URL: https://aws.amazon.com/blogs/machine-learning/aws-reaffirms-its-commitment-to-responsible-generative-ai/ Title: AWS Reaffirms its Commitment to Responsible Generative AI | AWS Machine Learning Blog

Permissions management is the undifferentiated heavy lifting of application development. Amazon Verified Permissions decouples authorization from application logic saving developers time and resources. Developers can reuse centralized policies as well as define and manage policies for their applications to accelerate time-to-market. Security and audit teams can better analyze and audit who has access to what using Verified Permissions. In this episode, Jillian is joined by Abhi Panday, Product Manager at AWS, to discuss how developers and security teams are using the service to build fine-grained authorization within their applications. Amazon Verified Permissions Blog: https://go.aws/3YduS2W Amazon Verified Permissions website: https://bit.ly/3O96j2n Amazon Verified Permissions resources: https://go.aws/3OvXtgx

It is sharp, informative, and fun! Simon, Hawn, and Jillian take you through all the latest AWS updates! Chapters: 00:47 AWS Marketplace 02:03 Analytics 03:57 Application Integration 04:00 Business Applications 05:26 Compute 06:47 Customer Engagement 07:30 Databases 08:57 Developer Tools 09:45 End User Computing 10:52 Front-End Web & Mobile 11:42 GameTech 12:17 Internet of Things (IoT) 12:27 Machine Learning 16:21 Management & Governance 17:42 Media Services 17:53 Migration & Transfer 19:26 Networking & Content Delivery 19:51 Security, Identity and Compliance 20:47 Storage Shownotes: https://d29iemol7wxagg.cloudfront.net/606ExtendedShownotes.html AWS Podcast Audio Feedback: https://bit.ly/3mDc3Y1

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