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April has been packed with new releases! Last week continued that trend with many new releases supporting a variety of domains such as security, analytics, devops, and many more, as well as more exciting new capabilities within generative AI. If you missed the AWS Summit London 2024, you can now watch the sessions on demand, including the keynote by Tanuja Randery, VP & Marketing Director, EMEA, and many of the break-out sessions which will continue to be released over the coming weeks. Last week’s launches Here are some of the highlights that caught my attention this week: Manual and automatic rollback from any stage in AWS CodePipeline – You can now rollback any stage, other than Source, to any previously known good state in if you use a V2 pipeline in AWS CodePipeline. You can configure automatic rollback which will use the source changes from the most recent successful pipeline execution in the case of failure, or you can initiate a manual rollback for any stage from the console, API or SDK and choose which pipeline execution you want to use for the rollback. AWS CodeArtifact now supports RubyGems – Ruby community, rejoice, you can now store your gems in AWS CodeArtifact! You can integrate it with RubyGems.org, and CodeArtifact will automatically fetch any gems requested by the client and store them locally in your CodeArtifact repository. That means that you can have a centralized place for both your first-party and public gems so developers can access their dependencies from a single source. Create a repository in AWS CodeArtifact and choose “rubygems-store” to connect your repository to RubyGems.org on the “Public upstream repositories” dropdown. Amazon EventBridge Pipes now supports event delivery through AWS PrivateLink – You can now deliver events to an Amazon EventBridge Pipes target without traversing the public internet by using AWS PrivateLink. You can poll for events in a private subnet in your Amazon Virtual Private Cloud (VPC) without having to deploy any additional infrastructure to keep your traffic private. Amazon Bedrock launches continue. You can now run scalable, enterprise-grade generative AI workloads with Cohere Command R & R+. And Amazon Titan Text V2 is now optimized for improving Retrieval-Augmented Generation (RAG). AWS Trusted Advisor – last year we launched Trusted Advisor APIs enabling you to programmatically consume recommendations. A new API is available now that you can use to exclude resources from recommendations. Amazon EC2 – there have been two new great launches this week for EC2 users. You can now mark your AMIs as “protected” to avoid them being deregistered by accident. You can also now easily discover your active AMIs by simply describing them. Amazon CodeCatalyst – you can now view your git commit history in the CodeCatalyst console. General Availability Many new services and capabilities became generally available this week. Amazon Q in QuickSight – Amazon Q has brought generative BI to Amazon QuickSight giving you the ability to build beautiful dashboards automatically simply by using natural language and it’s now generally available. To get started, head to the Quicksight Pricing page to explore all options or start a 30-day free trial which allows up to 4 users per QuickSight account to use all the new generative AI features. With the new generative AI features enabled by Amazon Q in Amazon QuickSight you can use natural language queries to build, sort and filter dashboards. (source: AWS Documentation) Amazon Q Business (GA) and Amazon Q Apps (Preview) – Also generally available now is Amazon Q Business which we launched last year at AWS re:Invent 2023 with the ability to connect seamlessly with over 40 popular enterprise systems, including Microsoft 365, Salesforce, Amazon Simple Storage Service (Amazon S3), Gmail, and so many more. This allows Amazon Q Business to know about your business so your employees can generate content, solve problems, and take actions that are specific to your business. We have also launched support for custom plug-ins, so now you can create your own integrations with any third-party application. With general availability of Amazon Q Business we have also launched the ability to create your own custom plugins to connect to any third-party API. Another highlight of this release is the launch of Amazon Q Apps, which enables you to quickly generate an app from your conversation with Amazon Q Business, or by describing what you would like it to generate for you. All guardrails from Amazon Q Business apply, and it’s easy to share your apps with colleagues through an admin-managed library. Amazon Q Apps is in preview now. Check out Channy Yun’s post for a deeper dive into Amazon Q Business and Amazon Q Apps, which guides you through these new features. Amazon Q Developer – you can use Q Developer to completely change your developer flow. It has all the capabilities of what was previously known as Amazon CodeWhisperer, such as Q&A, diagnosing common errors, generating code including tests, and many more. Now it has expanded, so you can use it to generate SQL, and build data integration pipelines using natural language. In preview, it can describe resources in your AWS account and help you retrieve and analyze cost data from AWS Cost Explorer. For a full list of AWS announcements, be sure to keep an eye on the ‘What’s New with AWS?‘ page. Other AWS news Here are some additional projects, blog posts, and news items that you might find interesting: AWS open source news and updates – My colleague Ricardo writes about open source projects, tools, and events from the AWS Community. Discover Claude 3 – If you’re a developer looking for a good source to get started with Claude 3 them I recommend this great post from my colleague Haowen Huang: Mastering Amazon Bedrock with Claude 3: Developer’s Guide with Demos. Upcoming AWS events Check your calendars and sign up for upcoming AWS events: AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Singapore (May 7), Seoul (May 16–17), Hong Kong (May 22), Milan (May 23), Stockholm (June 4), and Madrid (June 5). AWS re:Inforce – Explore 2.5 days of immersive cloud security learning in the age of generative AI at AWS re:Inforce, June 10–12 in Pennsylvania. AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Turkey (May 18), Midwest | Columbus (June 13), Sri Lanka (June 27), Cameroon (July 13), Nigeria (August 24), and New York (August 28). GOTO EDA Day London – Join us in London on May 14 to learn about event-driven architectures (EDA) for building highly scalable, fault tolerant, and extensible applications. This conference is organized by GOTO, AWS, and partners. Browse all upcoming AWS led in-person and virtual events and developer-focused events. That’s all for this week. Check back next Monday for another Weekly Roundup! — Matheus Guimaraes This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS! View the full article

Today, AWS announces the general availability of Amazon Q Business and the preview of Amazon Q Apps, a new Amazon Q Business capability. Amazon Q Business revolutionizes the way that employees interact with organizational knowledge and enterprise systems. It helps users get comprehensive answers to complex questions and take actions in a unified, intuitive web-based chat experience—all using an enterprise’s existing content, data, and systems. Amazon Q Business connects seamlessly to over 40 popular enterprise systems, including Amazon Simple Storage Service (Amazon S3), Microsoft 365, and Salesforce. It ensures that users access content securely with their existing credentials using single sign-on, according to their permissions, and enterprise-level access controls. View the full article

Today, AWS announces the general availability of Amazon Q Developer, a generative AI–powered assistant that reimagines your experience across the entire software development lifecycle (SDLC). Amazon Q Developer includes unique, game-changing capabilities that allow developers to offload time-consuming, manual tasks inside or outside of AWS. Amazon Q Developer capabilities include Q&A and diagnosing common errors in the AWS Management Console, Amazon Q data integration which enables you to build data integration pipelines using natural language, conversational coding and inline code generation in the IDE, and Amazon Q Developer Agent for software development in the IDE and in Amazon CodeCatalyst. Amazon Q Developer also includes Amazon Q Developer Agent for code transformation, a feature that can accelerate your application maintenance, upgrades, and migration in minutes. Additionally, Amazon Q Developer lists and describes resources in your AWS account (preview), and can now help you retrieve and analyze cost data from AWS Cost Explorer (preview). View the full article

Today, we’re excited to announce general availability of Amazon Q data integration in AWS Glue. Amazon Q data integration, a new generative AI-powered capability of Amazon Q Developer, enables you to build data integration pipelines using natural language. This reduces the time and effort you need to learn, build, and run data integration jobs using AWS Glue data integration engines. Tell Amazon Q Developer what you need in English, it will return a complete job for you. For example, you can ask Amazon Q Developer to generate a complete extract, transform, and load (ETL) script or code snippet for individual ETL operations. You can troubleshoot your jobs by asking Amazon Q Developer to explain errors and propose solutions. Amazon Q Developer provides detailed guidance throughout the entire data integration workflow. Amazon Q Developer helps you learn and build data integration jobs using AWS Glue efficiently by generating the required AWS Glue code based on your natural language descriptions. You can create jobs that extract, transform, and load data that is stored in Amazon Simple Storage Service (Amazon S3), Amazon Redshift, and Amazon DynamoDB. Amazon Q Developer can also help you connect to third-party, software as a service (SaaS), and custom sources. With general availability, we added new capabilities for you to author jobs using natural language. Amazon Q Developer can now generate complex data integration jobs with multiple sources, destinations, and data transformations. It can generate data integration jobs for extracts and loads to S3 data lakes including file formats like CSV, JSON, and Parquet, and ingestion into open table formats like Apache Hudi, Delta, and Apache Iceberg. It generates jobs for connecting to over 20 data sources, including relational databases like PostgreSQL, MySQL and Oracle; data warehouses like Amazon Redshift, Snowflake, and Google BigQuery; NoSQL databases like DynamoDB, MongoDB and OpenSearch; tables defined in the AWS Glue Data Catalog; and custom user-supplied JDBC and Spark connectors. Generated jobs can use a variety of data transformations, including filter, project, union, join, and custom user-supplied SQL. Amazon Q data integration in AWS Glue helps you through two different experiences: the Amazon Q chat experience, and AWS Glue Studio notebook experience. This post describes the end-to-end user experiences to demonstrate how Amazon Q data integration in AWS Glue simplifies your data integration and data engineering tasks. Amazon Q chat experience Amazon Q Developer provides a conversational Q&A capability and a code generation capability for data integration. To start using the conversational Q&A capability, choose the Amazon Q icon on the right side of the AWS Management Console. For example, you can ask, “How do I use AWS Glue for my ETL workloads?” and Amazon Q provides concise explanations along with references you can use to follow up on your questions and validate the guidance. To start using the AWS Glue code generation capability, use the same window. On the AWS Glue console, start authoring a new job, and ask Amazon Q, “Please provide a Glue script that reads from Snowflake, renames the fields, and writes to Redshift.” You will notice that the code is generated. With this response, you can learn and understand how you can author AWS Glue code for your purpose. You can copy/paste the generated code to the script editor and configure placeholders. After you configure an AWS Identity and Access Management (IAM) role and AWS Glue connections on the job, save and run the job. When the job is complete, you can start querying the table exported from Snowflake in Amazon Redshift. Let’s try another prompt that reads data from two different sources, filters and projects them individually, joins on a common key, and writes the output to a third target. Ask Amazon Q: “I want to read data from S3 in Parquet format, and select some fields. I also want to read data from DynamoDB, select some fields, and filter some rows. I want to union these two datasets and write the results to OpenSearch.” The code is generated. When the job is complete, your index is available in OpenSearch and can be used by your downstream workloads. AWS Glue Studio notebook experience Amazon Q data integration in AWS Glue helps you author code in an AWS Glue notebook to speed up development of new data integration applications. In this section, we walk you through how to set up the notebook and run a notebook job. Prerequisites Before going forward with this tutorial, complete the following prerequisites: Set up AWS Glue Studio. Configure an IAM role to interact with Amazon Q. Attach the following policy to your IAM role for the AWS Glue Studio notebook: { "Version": "2012-10-17", "Statement": [ { "Sid": "CodeWhispererPermissions", "Effect": "Allow", "Action": [ "codewhisperer:GenerateRecommendations" ], "Resource": "*" } ] } Create a new AWS Glue Studio notebook job Create a new AWS Glue Studio notebook job by completing the following steps: On the AWS Glue console, choose Notebooks under ETL jobs in the navigation pane. Under Create job, choose Notebook. For Engine, select Spark (Python). For Options, select Start fresh. For IAM role, choose the IAM role you configured as a prerequisite. Choose Create notebook. A new notebook is created with sample cells. Let’s try recommendations using the Amazon Q data integration in AWS Glue to auto-generate code based on your intent. Amazon Q would help you with each step as you express an intent in a Notebook cell. Add a new cell and enter your comment to describe what you want to achieve. After you press Tab and Enter, the recommended code is shown. First intent is to extract the data: “Give me code that reads a Glue Data Catalog table”, followed by “Give me code to apply a filter transform with star_rating>3” and “Give me code that writes the frame into S3 as Parquet”. Similar to the Amazon Q chat experience, the code is recommended. If you press Tab, then the recommended code is chosen. You can learn more in User actions. You can run each cell by simply filling in the appropriate options for your sources in the generated code. At any point in the runs, you can also preview a sample of your dataset by simply using the show() method. Let’s now try to generate a full script with a single complex prompt. “I have JSON data in S3 and data in Oracle that needs combining. Please provide a Glue script that reads from both sources, does a join, and then writes results to Redshift” You may notice that, on the notebook, the Amazon Q data integration in AWS Glue generated the same code snippet that was generated in the Amazon Q chat. You can also run the notebook as a job, either by choosing Run or programmatically. Conclusion With Amazon Q data integration, you have an artificial intelligence (AI) expert by your side to integrate data efficiently without deep data engineering expertise. These capabilities simplify and accelerate data processing and integration on AWS. Amazon Q data integration in AWS Glue is available in every AWS Region where Amazon Q is available. To learn more, visit the product page, our documentation, and the Amazon Q pricing page. A special thanks to everyone who contributed to the launch of Amazon Q data integration in AWS Glue: Alexandra Tello, Divya Gaitonde, Andrew Kim, Andrew King, Anshul Sharma, Anshi Shrivastava, Chuhan Liu, Daniel Obi, Hirva Patel, Henry Caballero Corzo, Jake Zych, Jeremy Samuel, Jessica Cheng, , Keerthi Chadalavada, Layth Yassin, Maheedhar Reddy Chappidi, Maya Patwardhan, Neil Gupta, Raghavendhar Vidyasagar Thiruvoipadi, Rajendra Gujja, Rupak Ravi, Shaoying Dong, Vaibhav Naik, Wei Tang, William Jones, Daiyan Alamgir, Japson Jeyasekaran, Matt Sampson, Kartik Panjabi, Ranu Shah, Chuan Lei, Huzefa Rangwala, Jiani Zhang, Xiao Qin, Mukul Prasad, Alon Halevy, Brian Ross, Alona Nadler, Omer Zaki, Rick Sears, Bratin Saha, G2 Krishnamoorthy, Kinshuk Pahare, Nitin Bahadur, and Santosh Chandrachood. About the Authors Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team. He is responsible for building software artifacts to help customers. In his spare time, he enjoys cycling with his road bike. Matt Su is a Senior Product Manager on the AWS Glue team. He enjoys helping customers uncover insights and make better decisions using their data with AWS Analytics services. In his spare time, he enjoys skiing and gardening. Vishal Kajjam is a Software Development Engineer on the AWS Glue team. He is passionate about distributed computing and using ML/AI for designing and building end-to-end solutions to address customers’ data integration needs. In his spare time, he enjoys spending time with family and friends. Bo Li is a Senior Software Development Engineer on the AWS Glue team. He is devoted to designing and building end-to-end solutions to address customers’ data analytic and processing needs with cloud-based, data-intensive technologies. XiaoRun Yu is a Software Development Engineer on the AWS Glue team. He is working on building new features for AWS Glue to help customers. Outside of work, Xiaorun enjoys exploring new places in the Bay Area. Savio Dsouza is a Software Development Manager on the AWS Glue team. His team works on distributed systems & new interfaces for data integration and efficiently managing data lakes on AWS. Mohit Saxena is a Senior Software Development Manager on the AWS Glue team. His team focuses on building distributed systems to enable customers with interactive and simple-to-use interfaces to efficiently manage and transform petabytes of data across data lakes on Amazon S3, and databases and data warehouses on the cloud. View the full article

Today, AWS announces general availability of Amazon Q data integration, a new generative AI–powered capability of Amazon Q Developer that enables you to build data integration pipelines using natural language. Amazon Q Developer is the AWS expert to assist you with all of your development tasks. Amazon Q data integration is a new chat experience specifically for AWS Glue, design for authoring and troubleshooting data integration pipelines. View the full article

Amazon Q in QuickSight is now generally available. The Generative BI capabilities of Amazon Q in QuickSight help business analysts and business users easily build and consume insights using natural language. View the full article

This was a busy week for Amazon Bedrock with many new features! Using GitHub Actions with AWS CodeBuild is much easier. Also, Amazon Q in Amazon CodeCatalyst can now manage more complex issues. I was amazed to meet so many new and old friends at the AWS Summit London. To give you a quick glimpse, here’s AWS Hero Yan Cui starting his presentation at the AWS Community stage. Last week’s launches With so many interesting new features, I start with generative artificial intelligence (generative AI) and then move to the other topics. Here’s what got my attention: Amazon Bedrock – For supported architectures such as Llama, Mistral, or Flan T5, you can now import custom models and access them on demand. Model evaluation is now generally available to help you evaluate, compare, and select the best foundation models (FMs) for your specific use case. You can now access Meta’s Llama 3 models. Agents for Amazon Bedrock – A simplified agent creation and return of control, so that you can define an action schema and get the control back to perform those action without needing to create a specific AWS Lambda function. Agents also added support for Anthropic Claude 3 Haiku and Sonnet to help build faster and more intelligent agents. Knowledge Bases for Amazon Bedrock – You can now ingest data from up to five data sources and provide more complete answers. In the console, you can now chat with one of your documents without needing to set up a vector database (read more in this Machine Learning blog post). Guardrails for Amazon Bedrock – The capability to implement safeguards based on your use cases and responsible AI policies is now available with new safety filters and privacy controls. Amazon Titan – The new watermark detection feature is now generally available in Amazon Bedrock. In this way, you can identify images generated by Amazon Titan Image Generator using an invisible watermark present in all images generated by Amazon Titan. Amazon CodeCatalyst – Amazon Q can now split complex issues into separate, simpler tasks that can then be assigned to a user or back to Amazon Q. CodeCatalyst now also supports approval gates within a workflow. Approval gates pause a workflow that is building, testing, and deploying code so that a user can validate whether it should be allowed to proceed. Amazon EC2 – You can now remove an automatically assigned public IPv4 address from an EC2 instance. If you no longer need the automatically assigned public IPv4 (for example, because you are migrating to using a private IPv4 address for SSH with EC2 instance connect), you can use this option to quickly remove the automatically assigned public IPv4 address and reduce your public IPv4 costs. Network Load Balancer – Now supports Resource Map in AWS Management Console, a tool that displays all your NLB resources and their relationships in a visual format on a single page. Note that Application Load Balancer already supports Resource Map in the console. AWS CodeBuild – Now supports managed GitHub Action self-hosted runners. You can configure CodeBuild projects to receive GitHub Actions workflow job events and run them on CodeBuild ephemeral hosts. Amazon Route 53 – You can now define a standard DNS configuration in the form of a Profile, apply this configuration to multiple VPCs, and share it across AWS accounts. AWS Direct Connect – Hosted connections now support capacities up to 25 Gbps. Before, the maximum was 10 Gbps. Higher bandwidths simplify deployments of applications such as advanced driver assistance systems (ADAS), media and entertainment (M&E), artificial intelligence (AI), and machine learning (ML). NoSQL Workbench for Amazon DynamoDB – A revamped operation builder user interface to help you better navigate, run operations, and browse your DynamoDB tables. Amazon GameLift – Now supports in preview end-to-end development of containerized workloads, including deployment and scaling on premises, in the cloud, or for hybrid configurations. You can use containers for building, deploying, and running game server packages. For a full list of AWS announcements, be sure to keep an eye on the What's New at AWS page. Other AWS news Here are some additional projects, blog posts, and news items that you might find interesting: GQL, the new ISO standard for graphs, has arrived – GQL, which stands for Graph Query Language, is the first new ISO database language since the introduction of SQL in 1987. Authorize API Gateway APIs using Amazon Verified Permissions and Amazon Cognito – Externalizing authorization logic for application APIs can yield multiple benefits. Here’s an example of how to use Cedar policies to secure a REST API. Build and deploy a 1 TB/s file system in under an hour – Very nice walkthrough for something that used to be not so easy to do in the recent past. Let’s Architect! Discovering Generative AI on AWS – A new episode in this amazing series of posts that provides a broad introduction to the domain and then shares a mix of videos, blog posts, and hands-on workshops. Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock – This post explores the new features (including AWS CloudFormation support) and how they align with the AWS Well-Architected Framework. Using the unified CloudWatch Agent to send traces to AWS X-Ray – With added support for the collection of AWS X-Ray and OpenTelemetry traces, you can now provision a single agent to capture metrics, logs, and traces. The executive’s guide to generative AI for sustainability – A guide for implementing a generative AI roadmap within sustainability strategies. AWS open source news and updates – My colleague Ricardo writes about open source projects, tools, and events from the AWS Community. Check out Ricardo’s page for the latest updates. Upcoming AWS events Check your calendars and sign up for upcoming AWS events: AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Singapore (May 7), Seoul (May 16–17), Hong Kong (May 22), Milan (May 23), Stockholm (June 4), and Madrid (June 5). AWS re:Inforce – Explore 2.5 days of immersive cloud security learning in the age of generative AI at AWS re:Inforce, June 10–12 in Pennsylvania. AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Turkey (May 18), Midwest | Columbus (June 13), Sri Lanka (June 27), Cameroon (July 13), Nigeria (August 24), and New York (August 28). GOTO EDA Day London – Join us in London on May 14 to learn about event-driven architectures (EDA) for building highly scalable, fault tolerant, and extensible applications. This conference is organized by GOTO, AWS, and partners. Browse all upcoming AWS led in-person and virtual events and developer-focused events. That’s all for this week. Check back next Monday for another Weekly Roundup! — Danilo This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS! View the full article

Today, we're excited to announce the availability of a new capability of Amazon Q to analyze issues for complexity and propose splitting the work into separate tasks. View the full article

Amazon Q feature development enables teams using Amazon CodeCatalyst to scale with AI to assist developers in completing everyday software development tasks. Developers can now go from an idea in an issue to a fully tested, merge-ready, running application code in a Pull Request (PR) with natural language inputs in a few clicks. Developers can also provide feedback to Amazon Q directly on the published pull request and ask it to generate a new revision. If the code change falls short of expectations, a new development environment can be created directly from the pull request, necessary adjustments can be made manually, a new revision published, and proceed with the merge upon approval. In this blog, we will walk through a use case leveraging the Modern three-tier web application blueprint, and adding a feature to the web application. We’ll leverage Amazon Q feature development to quickly go from Idea to PR. We also suggest following the steps outlined below in this blog in your own application so you can gain a better understanding of how you can use this feature in your daily work. Solution Overview Amazon Q feature development is integrated into CodeCatalyst. Figure 1 details how users can assign Amazon Q an issue. When assigning the issue, users answer a few preliminary questions and Amazon Q outputs the proposed approach, where users can either approve or provide additional feedback to Amazon Q. Once approved, Amazon Q will generate a PR where users can review, revise, and merge the PR into the repository. Figure 1: Amazon Q feature development workflow Prerequisites Although we will walk through a sample use case in this blog using a Blueprint from CodeCatalyst, after, we encourage you to try this with your own application so you can gain hands-on experience with utilizing this feature. If you are using CodeCatalyst for the first time, you’ll need: A CodeCatalyst space Amazon Q feature development in CodeCatalyst is currently in preview. To access this feature, ensure that you are using the Standard or Enterprise tier and the generative AI feature is enabled in your Space Walkthrough Step 1: Creating the blueprint In this blog, we’ll leverage the Modern three-tier web application blueprint to walk through a sample use case. This blueprint creates a Mythical Mysfits three-tier web application with modular presentation, application, and data layers. Figure 2: Creating a new Modern three-tier application blueprint First, within your space click “Create Project” and select the Modern three-tier web application CodeCatalyst Blueprint as shown above in Figure 2. Enter a Project name and select: Lambda for the Compute Platform and Amplify Hosting for Frontend Hosting Options. Additionally, ensure your AWS account is selected along with creating a new IAM Role. Once the project is finished creating, the application will deploy via a CodeCatalyst workflow, assuming the AWS account and IAM role were setup correctly. The deployed application will be similar to the Mythical Mysfits website. Step 2: Create a new issue The Product Manager (PM) has asked us to add a feature to the newly created application, which entails creating the ability to add new mythical creatures. The PM has provided a detailed description to get started. In the Issues section of our new project, click Create Issue For the Issue title, enter “Ability to add a new mythical creature” and for the Description enter “Users should be able to add a new mythical creature to the website. There should be a new Add button on the UI, when prompted should allow the user to fill in Name, Age, Description, Good/Evil, Lawful/Chaotic, Species, Profile Image URI and thumbnail Image URI for the new creature. When the user clicks save, the application should leverage the existing API in app.py to save the new creature to the DynamoDB table.” Furthermore, click Assign to Amazon Q as shown below in Figure 3. Figure 3: Assigning a new issue to Amazon Q Lastly, enable the Require Amazon Q to stop after each step and await review of its work. In this use case, we do not anticipate having any changes to our workflow files to support this new feature so we will leave the Allow Amazon Q to modify workflow files disabled as shown below in Figure 4. Click Create Issue and Amazon Q will get started. Figure 4: Configurations for assigning Amazon Q Step 3: Review Amazon Qs Approach After a few minutes, Amazon Q will generate its understanding of the project in the Background section as well as an Approach to make the changes for the issue you created as show in Figure 5 below (**Note: The Background and Approach generated for you may be different than what is shown in Figure 5 below). We have the option to proceed as is or can reply to the Approach via a Comment to provide feedback so Amazon Q can refine it to align better with the use case. Figure 5: Reviewing Amazon Qs Background and Approach In the approach, we notice Amazon Q is suggesting it will create a new method to create and save the new item to the table, but we already have an existing method. We decide to leave feedback as show in Figure 6 letting Amazon Q know the existing method should be leveraged. Figure 6: Provide feedback to Approach Amazon Q will now refine the approach based on the feedback provided. The refined approach generated by Amazon Q meets our requirements, including unit tests, so we decide to click Proceed as shown in Figure 7 below. Figure 7: Confirm approach and click Proceed Now, Amazon Q will generate the code for implementation & create a PR with code changes that can be reviewed. Step 4: Review the PR Within our project, under Code on the left panel click on Pull requests. You should see the new PR created by Amazon Q. The PR description contains the approach that Amazon Q took to generate the code. This is helpful to reviewers who want to gain a high-level understanding of the changes included in the PR before diving into the details. You will also be able to review all changes made to the code as shown below in Figure 8. Figure 8: Changes within PR Step 5 (Optional): Provide feedback on PR After reviewing the changes in the PR, I leave comments on a few items that can be improved. Notably, all fields on the new input form for creating a new creature should be required. After I complete leaving comments, I hit the Create Revision button. Amazon Q will take my comments, update the code accordingly and create a new revision of the PR as shown in Figure 9 below. Figure 9: PR Revision created. After reviewing the latest revision created by Amazon Q, I am happy with the changes and proceed with testing the changes directly from CodeCatalyst by utilizing Dev Environments. Once I have completed testing of the new feature and everything works as expected, we will let our peers review the PR to provide feedback and approve the pull request. As part of following the steps in this blog post, if you upgraded your Space to Standard or Enterprise tier, please ensure you downgrade to the Free tier to avoid any unwanted additional charges. Additionally, delete the project and any associated resources deployed in the walkthrough. Unassign Amazon Q from any issues no longer being worked on. If Amazon Q has finished its work on an issue or could not find a solution, make sure to unassign Amazon Q to avoid reaching the maximum quota for generative AI features. For more information, see Managing generative AI features and Pricing. Best Practices for using Amazon Q Feature Development You can follow a few best practices to ensure you experience the best results when using Amazon Q feature development: When describing your feature or issue, provide as much context as possible to get the best result from Amazon Q. Being too vague or unclear may not produce ideal results for your use case. Changes and new features should be as focused as possible. You will likely not experience the best results when making large and complex changes in a single issue. Instead, break the changes or feature up into smaller, more manageable issues where you will see better results. Leverage the feedback feature to practice giving input on approaches Amazon Q takes to ensure it gets to a similar outcome as highlighted in the blog. Conclusion In this post, you’ve seen how you can quickly go from Idea to PR using the Amazon Q Feature development capability in CodeCatalyst. You can leverage this new feature to start building new features in your applications. Check out Amazon CodeCatalyst feature development today. About the authors Brent Everman Brent is a Senior Technical Account Manager with AWS, based out of Pittsburgh. He has over 17 years of experience working with enterprise and startup customers. He is passionate about improving the software development experience and specializes in AWS’ Next Generation Developer Experience services. Brendan Jenkins Brendan Jenkins is a Solutions Architect at Amazon Web Services (AWS) working with Enterprise AWS customers providing them with technical guidance and helping achieve their business goals. He has an area of specialization in DevOps and Machine Learning technology. Fahim Sajjad Fahim is a Solutions Architect at Amazon Web Services. He helps customers transform their business by helping in designing their cloud solutions and offering technical guidance. Fahim graduated from the University of Maryland, College Park with a degree in Computer Science. He has deep interested in AI and Machine learning. Fahim enjoys reading about new advancements in technology and hiking. Abdullah Khan Abdullah is a Solutions Architect at AWS. He attended the University of Maryland, Baltimore County where he earned a degree in Information Systems. Abdullah currently helps customers design and implement solutions on the AWS Cloud. He has a strong interest in artificial intelligence and machine learning. In his spare time, Abdullah enjoys hiking and listening to podcasts. View the full article

We are excited to announce that Amazon Q for Business now supports metadata boosting for Q applications. This feature allows customers to easily fine-tune the underlying ranking of retrieved RAG passages in order to optimize Q&A answer relevance in their Q applications. View the full article

With all the generative AI announcements at AWS re:invent 2023, I’ve committed to dive deep into this technology and learn as much as I can. If you are too, I’m happy that among other resources available, the AWS community also has a space that I can access for generative AI tools and guides. Last week’s launches Here are some launches that got my attention during the previous week. Amazon Q data integration in AWS Glue (Preview) – Now you can use natural language to ask Amazon Q to author jobs, troubleshoot issues, and answer questions about AWS Glue and data integration. Amazon Q was launched in preview at AWS re:invent 2023, and is a generative AI–powered assistant to help you solve problems, generate content, and take action. General availability of CDK Migrate – CDK Migrate is a component of the AWS Cloud Development Kit (CDK) that enables you to migrate AWS CloudFormation templates, previously deployed CloudFormation stacks, or resources created outside of Infrastructure as Code (IaC) into a CDK application. This feature was launched alongside the CloudFormation IaC Generator to give you an end-to-end experience that enables you to create an IaC configuration based off a resource, as well as its relationships. You can expect the IaC generator to have a huge impact for a common use case we’ve seen. For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page. Other AWS news Here are some additional projects, programs, and news items that you might find interesting: Amazon API Gateway processed over 100 trillion API requests in 2023, demonstrating the growing demand for API-driven applications. API Gateway is a fully-managed API management service. Customers from all industry verticals told us they’re adopting API Gateway for multiple reasons. First, its ability to scale to meet the demands of even the most high-traffic applications. Second, its fully-managed, serverless architecture, which eliminates the need to manage any infrastructure, and frees customers to focus on their core business needs. Join the PartyRock Generative AI Hackathon by AWS. This is a challenge for you to get hands-on building generative AI-powered apps. You’ll use Amazon PartyRock, an Amazon Bedrock Playground, as a fast and fun way to learn about Prompt Engineering and Foundational Models (FMs) to build a functional app with generative AI. AWS open source news and updates – My colleague Ricardo writes this weekly open source newsletter in which he highlights new open source projects, tools, and demos from the AWS Community. Upcoming AWS events Whether you’re in the Americas, Asia Pacific & Japan, or EMEA region, there’s an upcoming AWS Innovate Online event that fits your timezone. Innovate Online events are free, online, and designed to inspire and educate you about AWS. AWS Summits are a series of free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. These events are designed to educate you about AWS products and services and help you develop the skills needed to build, deploy, and operate your infrastructure and applications. Find an AWS Summit near you and register or set a notification to know when registration opens for a Summit that interests you. AWS Community re:Invent re:Caps – Join a Community re:Cap event organized by volunteers from AWS User Groups and AWS Cloud Clubs around the world to learn about the latest announcements from AWS re:Invent. You can browse all upcoming in-person and virtual events. That’s all for this week. Check back next Monday for another Weekly Roundup! – Veliswa This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS! View the full article

Amazon Q is a generative-AI powered assistant that helps customers answer questions, provide summaries, generate content, and complete tasks based on data in their company repository. It also exists as a learning tool for AWS users who want to ask questions about services and best practices in the cloud. Amazon Q is integrated into AWS tools to assist readers and builders in learning services quickly, troubleshooting in the AWS Management Console, and much more, essentially working as an “AWS assistant” as users build. To use Amazon Q, you just need to sign into your AWS account and enter a question into the text bar in the Amazon Q panel. Amazon Q then generates a response to the question including a section with sources, that link to its references. After you receive a response, you can optionally leave feedback by using the thumbs-up and thumbs-down icons to strengthen the capabilities of the tool. In this blog, we’ll share how you, whether you’re technical or not, can use Amazon Q to accelerate and streamline your journey of learning how to build with AWS services. Use Amazon Q for AWS Documentation assistant Often, the first step in learning a new service is through that service’s front page and its documentation. These resources provide you with a foundation before you progress into hands on learning through building. As your cloud journey continues, documentation becomes an important tool in troubleshooting and customizing your workload. It’s no surprise though that many readers find AWS whitepapers and documentation long and complicated. As you read through a page you may run into an unknown technical term or an unfamiliar service feature. Rather than gear switching between multiple documents, you can now use the Amazon Q assistant to ask questions and get answers in real time! Just look for the Q icon on the right-hand side of any public AWS whitepaper, service front page, or documentation guide. You can see in the below example, while reading about best practices for snapshotting database clusters in Amazon Aurora, we want to understand if it is possible to automate the process. By asking Q, “Can I run automated snapshots on Amazon Aurora?” we receive concise details as well as link to the reference pages to learn more. I can ask quick clarifying questions and also receive targeted resources for further reading. Use the Amazon Q assistant to ask questions and get answers in real time! Just look for the Q icon on the right-hand side of any public AWS whitepaper, service front page, or documentation guide. As mentioned previously, Amazon Q is also available on each AWS service page. Below you can see we are on the Amazon Simple Storage Service (S3) service page and open up Amazon Q icon, which can also be found bottom right of the page. You are able to choose one of the prompts to get started or start asking Amazon Q service-specific questions in order to learn more about S3. By leveraging the Amazon Q chatbot to ask clarifying questions in real time, you no longer have to leave the page to dive deeper, providing a mechanism to help you remain focused. AWS Console assistant Your next step after reading documentation is likely to start building in the AWS Console. We often see that learners are kinesthetic and like to build as a way to better digest content, whether it’s through workshops, independent experimentation, or a guided in-person session. In these situations, there can often be more gear-shifting and/or getting lost in reading when a question arises mid-build. Now, you can find Amazon Q AWS expert in the console and ask your questions through the build process. Currently, Amazon Q AWS expert is in “preview” release and the use of expert assistant for AWS is available for no additional charges during the preview. This allows you to chat with the AWS expert Amazon Q assistant in the AWS Management Console, documentation, and AWS website. You can check out the additional details. You can find Amazon Q AWS expert in the AWS Management Console and ask your questions through the build process. After logging into the AWS Console, regardless of the service, you’ll find the Amazon Q icon on the right-hand side. The chatbot here functions in the same way as described above. Just type out your questions and Amazon Q will generate an answer with sources cited. In the console, learners have the opportunity to ask Amazon Q questions about AWS services, best practices, and even software development with the AWS SDKs and AWS CLI. Amazon Q in the console can generate short scripts or code snippets to help you get started using the AWS SDKs and AWS CLI. The following are example questions that demonstrate how Amazon Q can help you build on AWS and learn quickly: What’s the maximum runtime for an Amazon Lambda function? When should I put my resources in an Amazon VPC? What’s the best container service to use to run my workload if I need to keep my costs low? How do I list my Amazon S3 buckets? How do I create and host a website on AWS? In the console, you can ask Amazon Q questions about AWS services, best practices, and even software development with the AWS SDKs and AWS CLI. Amazon Q in the console can generate short scripts or code snippets to help you get started using the AWS SDKs and AWS CLI. Conclusion Whether you have just started reading about the cloud, or have been using AWS for a decade, keeping pace with the advances in cloud is a continuous learning journey. The more streamlined the process to ask clarifying questions during reading or building, the more efficient this journey becomes. In service of this, Amazon Q can help cut down the time it takes to find the right documentation and get your questions answered. If you have an AWS account, you can start using Amazon Q on any public documentation or in the AWS Console today. AWS sees security as top priority and have integrated responsible AI into the development of services like Amazon Q. We adhere to the AWS Responsible AI policy and we expect users to follow the same code of conduct. View the full article

Today we’re previewing a new chat experience for AWS Glue that will let you use natural language to author and troubleshoot data integration jobs. Amazon Q data integration in AWS Glue will reduce the time and effort you need to learn, build, and run data integration jobs using AWS Glue data integration engines. You can author jobs, troubleshoot issues, and get instant answers to questions about AWS Glue and anything related to data integration. The chat experience is powered by Amazon Bedrock. You can describe your data integration workload and Amazon Q will generate a complete ETL script. You can troubleshoot your jobs by asking Amazon Q to explain errors and propose solutions. Amazon Q provides detailed guidance throughout the entire data integration workflow. Amazon Q helps you learn and build data integration jobs using AWS Glue. Amazon Q can help you connect to common AWS sources such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, and Amazon DynamoDB. Let me show you some capabilities of Amazon Q data integration in AWS Glue. 1. Conversational Q&A capability To start using this feature, I can select the Amazon Q icon on the right-hand side of the AWS Management Console. For example, I can ask, “What is AWS Glue,” and Amazon Q provides concise explanations along with references I can use to follow up on my questions and validate the guidance. With Amazon Q, I can elaborate on my use cases in more detail to provide context. For example, I can ask Amazon Q, “How do I create an AWS Glue job?” Next let me ask Amazon Q, “How do I optimize memory management in my AWS Glue job?” 2. AWS Glue job creation To use this feature, I can tell Amazon Q, “Write a Glue ETL job that reads from Redshift, drops null fields, and writes to S3 as parquet files.” I can copy code into the script editor or notebook with a simple click on the Copy button. I can also tell Amazon Q, “Help me with a Glue job that reads my DynamoDB table, maps the fields, and writes the results to Amazon S3 in Parquet format”. Get started with Amazon Q today With Amazon Q, you have an artificial intelligence (AI) expert by your side to answer questions, write code faster, troubleshoot issues, optimize workloads, and even help you code new features. These capabilities simplify every phase of building applications on AWS. Amazon Q data integration in AWS Glue is available in every region where Amazon Q is supported. To learn more, see the Amazon Q pricing page. Learn more Amazon Q main product page Amazon Q data integration Amazon Q details for IT pros and developers Get started with Amazon Q — Irshad View the full article

AWS Step Functions expands its AWS SDK integrations with support for 33 additional AWS services including Amazon Q, AWS B2B Data Interchange, and Amazon CloudFront KeyValueStore. View the full article

Amazon Web Services (AWS) is announcing the public preview of Amazon Q in the AWS Console Mobile Application. AWS customers using the AWS Console Mobile App for iOS can now use Amazon Q, the generative AI assistant in the AWS Console Mobile App to ask questions about AWS services, and receive concise, reliable answers in a mobile-friendly user interface with voice input and output capabilities. Amazon Q in the AWS Console Mobile App is also available for Android (announced on 11/28/23). View the full article

AWS re:Invent, which has been taking place from November 27 and runs to December 1, has had its usual plethora of announcements: a total of 21 at time of print. Perhaps not surprisingly, given the huge potential impact of generative AI – ChatGPT officially turns one year old today – a lot of focus has... Read more » The post AWS re:Invent roundup: Next-gen chips, Amazon Q, and speedy S3 appeared first on Cloud Computing News. View the full article

Amazon Redshift introduces Amazon Q generative SQL in Amazon Redshift Query Editor, an out-of-the-box web-based SQL editor for Redshift, to simplify query authoring and increase your productivity by allowing you to express queries in natural language and receive SQL code recommendations. Furthermore, it allows you to get insights faster without extensive knowledge of your organization’s complex database metadata. View the full article

Amazon Redshift puts artificial intelligence (AI) at your service to optimize efficiencies and make you more productive with two new capabilities that we are launching in preview today. First, Amazon Redshift Serverless becomes smarter. It scales capacity proactively and automatically along dimensions such as the complexity of your queries, their frequency, the size of the dataset, and so on to deliver tailored performance optimizations. This allows you to spend less time tuning your data warehouse instances and more time getting value from your data. Second, Amazon Q generative SQL in Amazon Redshift Query Editor generates SQL recommendations from natural language prompts. This helps you to be more productive in extracting insights from your data. Let’s start with Amazon Redshift Serverless When you use Amazon Redshift Serverless, you can now opt in for a preview of AI-driven scaling and optimizations. When enabled, the system observes and learns from your usage patterns, such as the concurrent number of queries, their complexity, and the time it takes to run them. Then, it automatically optimizes your serverless endpoint to meet your price performance target. Based on AWS internal testing, this new capability may give you up to ten times better price performance for variable workloads without any manual intervention. AI-driven scaling and optimizations eliminate the time and effort to manually resize your workgroup and plan background optimizations based on workload needs. It continually runs automatic optimizations when they are most valuable for better performance, avoiding performance cliffs and time-outs. This new capability goes beyond the existing self-tuning capabilities of Amazon Redshift Serverless, such as machine learning (ML)-enhanced techniques to adjust your compute, modify the physical schema of the database, create or drop materialized views as needed (the one we manage automatically, not yours), and vacuum tables. This new capability brings more intelligence to decide how to adjust the compute, what background optimizations are required, and when to apply them, and it makes its decisions based on more dimensions. We also orchestrate ML-based optimizations for materialized views, table optimizations, and workload management when your queries need it. During the preview, you must opt in to enable these AI-driven scaling and optimizations on your workgroups. You configure the system to balance the optimization for price or performance. There is only one slider to adjust in the console. As usual, you can track resource usage and associated changes through the console, Amazon CloudWatch metrics, and the system table SYS_SERVERLESS_USAGE. Now, let’s look at Amazon Q generative SQL in Amazon Redshift Query Editor What if you could use generative AI to help analysts write effective SQL queries more rapidly? This is the new experience we introduce today in Amazon Redshift Query Editor, our web-based SQL editor. You can now describe the information you want to extract from your data in natural language, and we generate the SQL query recommendations for you. Behind the scenes, Amazon Q generative SQL uses a large language model (LLM) and Amazon Bedrock to generate the SQL query. We use different techniques, such as prompt engineering and Retrieval Augmented Generation (RAG), to query the model based on your context: the database you’re connected to, the schema you’re working on, your query history, and optionally the query history of other users connected to the same endpoint. The system also remembers previous questions. You can ask it to refine a previously generated query. The SQL generation model uses metadata specific to your data schema to generate relevant queries. For example, it uses the table and column names and the relationship between the tables in your database. In addition, your database administrator can authorize the model to use the query history of all users in your AWS account to generate even more relevant SQL statements. We don’t share your query history with other AWS accounts and we don’t train our generation models with any data coming from your AWS account. We maintain the high level of privacy and security that you expect from us. Using generated SQL queries helps you to get started when discovering new schemas. It does the heavy lifting of discovering the column names and relationships between tables for you. Senior analysts also benefit from asking what they want in natural language and having the SQL statement automatically generated. They can review the queries and run them directly from their notebook. Let’s explore a schema and extract information For this demo, let’s pretend I am a data analyst at a company that sells concert tickets. The database schema and data are available for you to download. My manager asks me to analyze the ticket sales data to send a thank you note with discount coupons to the highest-spending customers in Seattle. I connect to Amazon Redshift Query Editor and connect the analytic endpoint. I create a new tab for a Notebook (SQL generation is available from notebooks only). Instead of writing a SQL statement, I open the chat panel and type, “Find the top five users from Seattle who bought the most number of tickets in 2022.” I take the time to verify the generated SQL statement. It seems correct, so I decide to run it. I select Add to notebook and then Run. The query returns the list of the top five buyers in Seattle. I had no previous knowledge of the data schema, and I did not type a single line of SQL to find the information I needed. But generative SQL is not limited to a single interaction. I can chat with it to dynamically refine the queries. Here is another example. I ask “Which state has the most venues?” Generative SQL proposes the following query. The answer is New York, with 49 venues, if you’re curious. I changed my mind, and I want to know the top three cities with the most venues. I simply rephrase my question: “What about the top three venues?” I add the query to the notebook and run it. It returns the expected result. Best practices for prompting Here are a couple of tips and tricks to get the best results out of your prompts. Be specific – When asking questions in natural language, be as specific as possible to help the system understand exactly what you need. For example, instead of writing “find the top venues that sold the most tickets,” provide more details like “find the names of the top three venues that sold the most tickets in 2022.” Use consistent entity names like venue, ticket, and location instead of referring to the same entity in different ways, which can confuse the system. Iterate – Break your complex requests into multiple simple statements that are easier for the system to interpret. Iteratively ask follow-up questions to get more detailed analysis from the system. For example, start by asking, “Which state has the most venues?” Then, based on the response, ask a follow-up question like “Which is the most popular venue from this state?” Verify – Review the generated SQL before running it to ensure accuracy. If the generated SQL query has errors or does not match your intent, provide instructions to the system on how to correct it instead of rephrasing the entire request. For example, if the query is missing a filter clause on year, write “provide venues from year 2022.” Availability and pricing AI-driven scaling and optimizations are in preview in six AWS Regions: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland, Stockholm). They come at no additional cost. You pay only for the compute capacity your data warehouse consumes when it is active. Pricing is per Redshift Processing Unit (RPU) per hour. The billing is per second of used capacity. The pricing page for Amazon Redshift has the details. Amazon Q generative SQL for Amazon Redshift Query Editor is in preview in two AWS Regions today: US East (N. Virginia) and US West (Oregon). There is no charge during the preview period. These are two examples of how AI helps to optimize performance and increase your productivity, either by automatically adjusting the price-performance ratio of your Amazon Redshift Serverless endpoints or by generating correct SQL statements from natural language prompts. Previews are essential for us to capture your feedback before we make these capabilities available for all. Experiment with these today and let us know what you think on the re:Post forums or using the feedback button on the bottom left side of the console. -- sebView the full article

Today, we are excited to announce the availability of Amazon Q's feature development capability in preview, in Amazon CodeCatalyst. With this new capability, developers can assign a CodeCatalyst issue to Amazon Q, and Q performs the heavy lifting of converting a human prompt to an actionable plan, then completes code changes and a pull request that is assigned to the requester. Q will then monitor any associated workflows and attempt to correct any issues. The user can preview code changes and merge the pull request. Development teams can utilize this new capability as an end-to-end, streamlined experience within Amazon CodeCatalyst, without having to enter the IDE. View the full article

Now AWS gets a ChatGPT-style Copilot: Amazon Q to be your cloud chat assistantView the full article

Amazon Web Services (AWS) has become the latest technology giant to unveil a workplace assistant powered by generative AI. Revealed by CEO Adam Selipsky at its AWS re:Invent 2023 event in Las Vegas, Amazon Q is designed specifically for business use, allowing employees to ask complex questions about their specific work tasks and get detailed responses. The company believes Amazon Q can help workers at every level, in roles from developers to marketing to call center staff, save huge amounts of time and stress by providing exactly the assistance needed. Amazon Q Speaking at his AWS re:Invent keynote, Selipsky noted that Amazon Q will also be "your business expert,” learning about your business as you connect to it. The platform connects to over 40 popular tools such as Salesforce, Gmail, Slack, Atlassian and Microsoft 365, taking in and indexing all this connected data and content, as well as identifying business-specific aspects such as its organizational structure and even product names. (Image credit: AWS) Trained on 17 years of AWS knowledge, Amazon Q can be specifically tailored and customized to the precise tasks you encounter at work, with conversational prompts and questions resulting in detailed answers provided in nearly real-time. Selipsky noted that developers and IT workers in particular are being tasked with keeping pace with the rapidly changing and evolving technology industry, with generative AI moving faster than ever before. This innovation may be great for some, but ordinary workers are often catching the brunt of the demand for new features and upgrades, meaning they find it hard to balance new demands with existing workloads. "I really believe this is going to be transformative," Selipsky said of Amazon Q, "This is just the start of how we're going to revolutionize the future of work." The Q Architect service can also help you research, troubleshoot and analyze issues across your business, saving time and stress, and provide suggestions on optimizing AWS infrastructure queries, such as EC2 instances. Amazon Q is also going to be in the IDE for developers, working alongside CodeWhisperer for further code suggestions, helping reduce "hours of work...it'll do all the heavy lifting for you," Selipsky says. Amazon Q will be available today in preview for existing AWS customers, with a wider release coming sometime in the future. More from TechRadar Pro Looking for a bit more capacity? Here's the best cloud storage choicesAnd we've also rounded up the best cloud hosting providersAmazon wants to train millions of people in basic AI skills View the full article

Amazon Q, a generative AI–powered assistant that is specifically designed for work and can be tailored to your business, is available in Amazon Connect. Amazon Q in Connect delivers real-time recommendations that help contact center agents resolve customer issues quickly and accurately, helping improve both agent productivity and customer satisfaction. This generative AI assistant, which includes functionality formerly available as Amazon Connect Wisdom, can help agents detect customer intent during calls and chats using conversational analytics and natural language understanding (NLU), then provides agents with generated responses and suggested actions, along with links to relevant documents and articles. For example, Amazon Q can help detect that a customer is contacting a rental car company to change their reservation, generate a response for the agent to quickly communicate how the company’s change fee policies apply to this customer, and guide the agent through the steps they need to update the reservation. In addition to receiving recommendations, agents can also query Amazon Q using natural language or keywords to answer customer requests. Amazon Q is built into the Amazon Connect agent workspace and is available via API for your own agent desktop. View the full article

Today, AWS announces a range of capabilities in preview in Amazon Q to supercharge work for developers and IT professionals and provide expert assistance when building, deploying, and operating applications and workloads on AWS. Amazon Q is a generative AI-powered assistant that can be tailored to your business, code, data, and operations. View the full article

Today, AWS announces Amazon Q, a new generative AI–powered assistant that is specifically designed for work and can be tailored to your business to have conversations, solve problems, generate content, and take actions using the data and expertise found in your company’s information repositories, code, and enterprise systems. View the full article

If you manage a contact center, then you know the critical role that agents play in helping your organization build customer trust and loyalty. Those of us who’ve reached out to a contact center know how important agents are with guiding through complex decisions and providing fast and accurate solutions where needed. This can take time, and if not done correctly, then it may lead to frustration. Generative AI capabilities in Amazon Connect Today, we’re announcing that the existing artificial intelligence (AI) features of Amazon Connect now have generative AI capabilities that are powered by large language models (LLMs) available through Amazon Bedrock to transform how contact centers provide service to customers. LLMs are pre-trained on vast amounts of data, commonly known as foundation models (FMs), and they can understand and learn, generate text, engage in interactive conversations, answer questions, summarize dialogs and documents, and provide recommendations. Amazon Q in Connect: recommended responses and actions for faster customer support Organizations are in a state of constant change. To maintain a high level of performance that keeps up with these organizational changes, contact centers continuously onboard, train, and coach agents. Even with training and coaching, agents must often search through different sources of information, such as product guides and organization policies, to provide exceptional service to customers. This can increase customer wait times, lowering customer satisfaction and increasing contact center costs. Amazon Q in Connect, a generative AI-powered agent assistant that includes functionality formerly available as Amazon Connect Wisdom, understands customer intents and uses relevant sources of information to deliver accurate responses and actions for the agent to communicate and resolve unique customer needs, all in real-time. Try Amazon Q in Connect for no charge until March 1, 2024. The feature is easy to enable, and you can get started in the Amazon Connect console. Amazon Connect Contact Lens: generative post-contact summarization for increased productivity To improve customer interactions and make sure details are available for future reference, contact center managers rely on the notes that agents manually create after every customer interaction. These notes include details on how a customer issue was addressed, key moments of the conversation, and any pending follow-up items. Amazon Connect Contact Lens now provides generative AI-powered post-contact summarization, and enables contact center managers to more efficiently monitor and help improve contact quality and agent performance. For example, you can use summaries to track commitments made to customers and make sure of the prompt completion of follow-up actions. Moments after a customer interaction, Contact Lens now condenses the conversation into a concise and coherent summary. Amazon Lex in Amazon Connect: assisted slot resolution Using Amazon Lex, you can already build chatbots, virtual agents, and interactive voice response (IVR) which lets your customers schedule an appointment without speaking to a human agent. For example, “I need to change my travel reservation for myself and my two children,” might be difficult for a traditional bot to resolve to a numeric value (how many people are on the travel reservation?). With the new assisted slot resolution feature, Amazon Lex can now resolve slot values in user utterances with great accuracy (for example, providing an answer to the previous question by providing a correct numeric value of three). This is powered by the advanced reasoning capabilities of LLMs which improve accuracy and provide a better customer experience. Learn about all the features of Amazon Lex, including the new generative AI-powered capabilities to help you build better self-service experiences. Amazon Connect Customer Profiles: quicker creation of unified customer profiles for personalized customer experiences Customers expect personalized customer service experiences. To provide this, contact centers need a comprehensive understanding of customers’ preferences, purchases, and interactions. To achieve that, contact center administrators create unified customer profiles by merging customer data from a number of applications. These applications each have different types of customer data stored in varied formats across a range of data stores. Stitching together data from these various data stores needs contact center administrators to understand their data and figure out how to organize and combine it into a unified format. To accomplish this, they spend weeks compiling unified customer profiles. Starting today, Amazon Connect Customer Profiles uses LLMs to shorten the time needed to create unified customer profiles. When contact center administrators add data sources such as Amazon Simple Storage Service (Amazon S3), Adobe Analytics, Salesforce, ServiceNow, and Zendesk, Customer Profiles analyze the data to understand what the data format and content represents and how the data relates to customers’ profiles. Then, Customer Profiles then automatically determines how to organize and combine data from different sources into complete, accurate profiles. With just a few steps, managers can review, make any necessary edits, and complete the setup of customer profiles. In-app, web, and video capabilities in Amazon Connect As an organization, you want to provide great, easy-to-use, and convenient customer service. Earlier in this post I talked about self-service chatbots and how they help you with this. At times customers want to move beyond the chatbot, and beyond an audio conversation with the agent. Amazon Connect now has in-app, web, and video capabilities to help you deliver rich, personalized customer experiences. Using the fully-managed communication widget, and with a few lines of code, you can implement these capabilities on your web and mobile applications. This allows your customers to get support from a web or mobile application without ever having to leave the page. Video can be enabled by either the agent only, by the customer only, or by both agent and customer. Amazon Connect SMS: two-way SMS capabilities Almost everyone owns a mobile device and we love the flexibility of receiving text-based support on-the-go. Contact center leaders know this, and in the past have relied on disconnected, third-party solutions to provide two-way SMS to customers. Amazon Connect now has two-way SMS capabilities to enable contact center leaders to provide this flexibility. This improves customer satisfaction and increases agent productivity without costly integration with third-party solutions. SMS chat can be enabled using the same configuration, Amazon Connect agent workspace, and analytics as calls and chats. Learn more Amazon Q in Connect product page Getting started with Amazon Connect user guide Send feedback AWS re:Post for Amazon Connect, or through your usual AWS support contacts – Veliswa View the full article