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New Strategy Recommendations Service Helps Streamline AWS Cloud Migration and Modernization

Determining viable strategies for successful application migration and modernization to the cloud takes time. It can also require significant effort, depending on the size and complexity of the application portfolio to analyze. To date, the analysis process has been largely manual and nonstandard in nature, making it difficult to apply at scale on large portfolios.…

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Determining viable strategies for successful application migration and modernization to the cloud takes time. It can also require significant effort, depending on the size and complexity of the application portfolio to analyze. To date, the analysis process has been largely manual and nonstandard in nature, making it difficult to apply at scale on large portfolios. Limited time to make decisions, a lack of domain knowledge and cloud expertise, and low awareness of the available modernization tools and services can compound the effort and complexity.

Today, I’m pleased to announce AWS Migration Hub Strategy Recommendations to help automate the analysis of your application portfolios. Strategy Recommendations analyzes your running applications to determine runtime environments and process dependencies, optionally analyzes source code and databases, and more. The data collected from analysis is assessed against a set of business objectives that you prioritize, such as license cost reduction, speed of migration, reducing operational overhead from using managed services, or modernizing infrastructure using cloud-native technologies. Then, it produces recommendations of viable paths to migrate and modernize your applications.

Any given application could have multiple paths for migration and modernization, including rehosting, replatforming, or refactoring. You’ll get recommendations on all viable paths, and you can elect to override the recommendations as you see fit. Everyone can use Strategy Recommendations, regardless of experience, to lower the effort and time required and complexity involved in assessing application portfolios, whether they’re on premises awaiting migration or already in the AWS Cloud pending further modernization.

Taking as an example a typical N-tier application, an ASP.NET web application with a Microsoft SQL Server database, Strategy Recommendations helps you analyze the various components such as the servers hosting the web front end, the backend servers, and the database itself to determine viable paths and tools you can use to migrate and modernize onto the AWS Cloud. For instance, if your goal is to reduce licensing costs for the application, Strategy Recommendations may recommend you to refactor your application to .NET on Linux using the Porting Assistant for .NET.

Registering your Application Servers for Strategy Recommendations
Registration of the servers hosting your application portfolio with AWS Application Discovery Service is a prerequisite for Strategy Recommendations. The servers to register can be running on-premises as physical servers or virtual machines (VMs), or they can be Amazon Elastic Compute Cloud (Amazon EC2) instances for applications you’ve already migrated with a “lift-and-shift” process. You can find details on the different options for registering your application servers in the AWS Application Discovery Service User Guide.

Automated Data Collection for Analysis
With your servers registered in AWS Application Discovery Service, you can set up automated collection of the process level analysis of your application portfolio using an agentless data collector provided by Strategy Recommendations. The agentless collector can be downloaded as an Open Virtualization Appliance (OVA) for VMWare vCenter environments. If you’ve already migrated some or all of your applications to EC2, there’s also an EC2 Amazon Machine Image (AMI), which includes the collector, to help further analyze these applications for modernization opportunities.

If you don’t want, or cannot use, automated collection methods, or you’ve already collected this data using another tool or service, then you can instead manually import the data for analysis. However, the recommendations you obtain for manually imported data won’t be as in-depth as those originating from automated data collection. One additional benefit of automated collection is that it’s much easier to refresh the data as you progress, too.

Application and process discovery on your servers is language-agnostic. For .NET and Java applications in GitHub and GitHub Enterprise repositories and Microsoft SQL Server databases, you can optionally include detection of cloud anti-patterns. It’s important to note that if you elect to have source code or database analysis performed, no actual code or data is uploaded to Strategy Recommendations; only the results of the analysis are sent. By the way, if you elect to manually import your data for analysis, the option to perform deeper source code and database analysis is not supported.

Analyzing your Application Portfolio
Full details on how to set up automated data collection, the analysis options, and other important prerequisites can be found in the Strategy Recommendations User Guide, so I won’t go into further detail here. Instead, I want to look at how you can start analyzing an application portfolio that’s already been migrated to EC2, with an intent to modernize further, using the agentless collector. As mentioned earlier, Strategy Recommendations supports analysis of application portfolios hosted on physical on-premises servers or virtual machines, or (as shown in this post) on EC2 instances.

To start collection of data for analysis, I need to follow a small number of steps:

  1. Start and configure the Strategy Recommendations agentless collector, using either the downloadable OVA or the provided EC2 AMI.
  2. Configure each of the Windows and Linux instances hosting my applications to allow access from the collector.
  3. Configure my initial business priorities and other application and database preferences to get my initial recommendations. I can fine-tune these options later.

My first stop is at the Migration Hub console, where I click Strategy in the navigation panel to take me to the Get started page. On clicking any of the Download data collector, Download import template, or Get recommendations buttons, I’m first asked to agree to the creation of a service-linked role, granting Strategy Recommendations the necessary permissions to access other services on my behalf. Once I agree, I start at the Configure data sources page of a short wizard. Here, I can view a list of any previously registered collectors. I can also download the OVA version of the data collector and an import template for any application data I want to import manually, outside of automated collection.

Initial data source configuration page

I’m going to use the EC2 AMI-based collector so, before proceeding with this wizard, I open the EC2 console in a new browser tab to launch it. To find the image for the Strategy Recommendations data collector I can either go to the AMIs page, select Public images, and filter by owner 703163444405, or, from the Launch Instances wizard, enter the name AWSMHubApplicationDataCollector in the Search field. Once I’ve found the image, I proceed through the launch wizard as I would for any other AMI.

Configuration of the collector is a simple process, and I’m guided using a series of questions. As I mentioned earlier, full information is in the user guide that I linked to, so I won’t go into every detail here. To start the configuration process, I first use SSH to connect to my collector instance and then run a Docker container, using the command docker exec -it application-data-collector bash. In the running container, I start the configuration Q&A with the command collector setup. During the process, you’re asked to supply data for the following items of information:

  1. Usage agreement and confirmation that all required roles have been set up, followed by a set of AWS access and secret keys.
  2. For on-premises Windows application servers that are not managed by vCenter, or EC2 Windows instances, I need to provide a user ID and password that will allow the collector to connect to my servers using WinRM.
  3. If I have any Linux application servers, I can choose whether the collector connects using SSH or certificate-based authentication.
  4. Finally, I can configure source code analysis for .NET and Java applications in repositories on GitHub or GitHub Enterprise. These require a Git username and personal access token (PAT). I can also configure additional, deeper, source code analysis for C# applications. This does, however, require a separate server running Windows, on which I’ve installed the Porting Assistant for .NET.

Once I have completed these steps, my data collector is registered and ready to start inspecting my servers. Back on the Strategy Recommendations Configure data sources page, I refresh the page and can now see my collector listed.

Registered data collector

The second step is to enable access from the collector to my application servers, details for which can be found in the Step 4: Set up the Strategy Recommendations collector topic of the user guide. For my Windows Server, I used RDP to connect and then downloaded and ran two PowerShell scripts from links provided in the guide to configure WinRM. For larger server fleets, you might consider using AWS Systems Manager Automation to perform this task. For my Linux servers, having chosen to use SSH authentication for the collector, I needed to copy public key material generated during collector configuration process to each server.

At this point, the servers to be analyzed are known to AWS Application Discovery Service, the Strategy Recommendations data collector is configured, and each server is configured to allow access from the collector. It’s now time for my third and final step; namely, to set my business and other priorities for the analysis and let the service get to work to generate my recommendations.

Back in the Get started page in Strategy Recommendations, since my collector is registered and I have no manual application data to import, I just choose Next. This takes me to the Specify Preferences page, where I set my business priorities and other preferences. I can revise these and reanalyze at any time, but for now, I use drag and drop to set License cost reduction, Modernizing infrastructure using cloud-native technologies, and Reduce operational overhead with managed services as my highest priorities. I leave the remaining options, for application and database preferences, unchanged.

Configuring my business priorities and other settings for the analysis recommendations

Choosing Next, I reach the Review page, summarizing my choices, then choose Start data analysis. One item of note, the analysis runs against all servers that you’ve configured in Application Discovery Service, so you may see more servers being processed than you imported in the earlier step (servers not configured to allow access by the collector show up in results with a collection status of “data collection failed”).

With analysis complete, my recommendations are summarized (no anti-pattern analysis has been run yet).

Initial recommendations from server analysis

One of my servers is running Windows and hosts an older version of nopCommerce, originally a .NET Framework-based application, and a related SQL Server database. As my highest business priority was license cost reduction, I start my inspection at that server. The recommendations available so far are based on inspection of just the server itself. Analysis of the source code and components comprising the application may likely influence those recommendations, so I request further analysis of the application source code by drilling down to the server and application of interest.

Adding source code analysis for the server application

Code analysis creates a JSON-format report file in Amazon Simple Storage Service (Amazon S3), which when I open it, shows anti-patterns such as accessing log files using Windows file system paths instead of a cloud-based service such as Amazon CloudWatch, fixed IP addresses, a server-specific database connection, and more.

Following code analysis, the suggested recommendations update slightly from those based on just inspection of the servers. One application component that was originally recommended for a replatforming approach is now a candidate for refactoring.

Revised recommendations

Returning to my server of interest, clicking the Strategy options tab shows me the recommendations. The results of the code analysis have played a part in the weightings, along with my business priorities. The image below shows the initial recommendations, which are based on just analysis of the server itself.

The initial recommendations

Below are the revised recommendations for the server, following source code analysis.

Revised recommendations following source code analysis

The recommendations for the server also include replatforming the application’s SQL Server database to MySQL on Amazon Relational Database Service (RDS). This is suggested because in my priorities, I requested consideration of managed services. Before following this recommendation I may want to perform an additional anti-pattern analysis of the database, which I can do after creating a secret in AWS Secrets Manager to hold the database credentials (check the user guide topic on database analysis for more details). Analysis of databases, which is currently only available for SQL Server, identifies migration incompatibilities such as unsupported data types.

In the screenshots, you’ll notice additional viable paths for migration and modernization. This applies to both servers and application components. I can choose a viable path over the recommended strategy if I so want by selecting the viable strategy option and clicking Set preferred. In the screenshot below, for the nopCommerce application component, I’ve chosen to prefer the replatform route to containers for the application, using AWS App2Container. And of course, I can always rewind to the start and adjust my business priorities and other options and reanalyze my data.

Setting a preferred approach for a recommendation

Taking the initial recommendations, then using code and database analysis, or revising your priorities for analysis and the suggested recommendations, provides scope to experiment with multiple “what if” options to discover the optimal strategy for migrating and modernizing application portfolios to the cloud. Once that optimal strategy is determined, you can communicate it to downstream teams to begin the migration and modernization process for your application portfolio.

Get Recommendations for Migration and Modernization Today
You can get started analyzing your servers and application portfolios today with AWS Migration Hub Strategy Recommendations, at no extra charge, in the US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), and Europe (London) Regions. You can, of course, deploy the applications you choose to migrate and modernize based on recommendations from the tool to all Regions. As I noted earlier, you can find more details on prerequisites, getting started with the collector, and working with recommendations in the user guide.

— Steve

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AWS Week in Review – May 16, 2022

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you…

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This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you in person. The Serverless Developer Advocates are going around many of the AWS Summits with the Serverlesspresso booth. If you attend an event that has the booth, say “Hi ” to my colleagues, and have a coffee while asking all your serverless questions. You can find all the upcoming AWS Summits in the events section at the end of this post.

Last week’s launches
Here are some launches that got my attention during the previous week.

AWS Step Functions announced a new console experience to debug your state machine executions – Now you can opt-in to the new console experience of Step Functions, which makes it easier to analyze, debug, and optimize Standard Workflows. The new page allows you to inspect executions using three different views: graph, table, and event view, and add many new features to enhance the navigation and analysis of the executions. To learn about all the features and how to use them, read Ben’s blog post.

Example on how the Graph View looks

Example on how the Graph View looks

AWS Lambda now supports Node.js 16.x runtime – Now you can start using the Node.js 16 runtime when you create a new function or update your existing functions to use it. You can also use the new container image base that supports this runtime. To learn more about this launch, check Dan’s blog post.

AWS Amplify announces its Android library designed for Kotlin – The Amplify Android library has been rewritten for Kotlin, and now it is available in preview. This new library provides better debugging capacities and visibility into underlying state management. And it is also using the new AWS SDK for Kotlin that was released last year in preview. Read the What’s New post for more information.

Three new APIs for batch data retrieval in AWS IoT SiteWise – With this new launch AWS IoT SiteWise now supports batch data retrieval from multiple asset properties. The new APIs allow you to retrieve current values, historical values, and aggregated values. Read the What’s New post to learn how you can start using the new APIs.

AWS Secrets Manager now publishes secret usage metrics to Amazon CloudWatch – This launch is very useful to see the number of secrets in your account and set alarms for any unexpected increase or decrease in the number of secrets. Read the documentation on Monitoring Secrets Manager with Amazon CloudWatch for more information.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other launches and news that you may have missed:

IBM signed a deal with AWS to offer its software portfolio as a service on AWS. This allows customers using AWS to access IBM software for automation, data and artificial intelligence, and security that is built on Red Hat OpenShift Service on AWS.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish. This week’s episode introduces you to Amazon DynamoDB and shares stories on how different customers use this database service. You can listen to all the episodes directly from your favorite podcast app or the podcast web page.

AWS Open Source News and Updates – Ricardo Sueiras, my colleague from the AWS Developer Relation team, runs this newsletter. It brings you all the latest open-source projects, posts, and more. Read edition #112 here.

Upcoming AWS Events
It’s AWS Summits season and here are some virtual and in-person events that might be close to you:

You can register for re:MARS to get fresh ideas on topics such as machine learning, automation, robotics, and space. The conference will be in person in Las Vegas, June 21–24.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia



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Personalize your machine translation results by using fuzzy matching with Amazon Translate

A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when…

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A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when it comes to professional language translation. Customers of translation systems and services expect accurate and highly customized outputs. To achieve this, they often reuse previous translation outputs—called translation memory (TM)—and compare them to new input text. In computer-assisted translation, this technique is known as fuzzy matching. The primary function of fuzzy matching is to assist the translator by speeding up the translation process. When an exact match can’t be found in the TM database for the text being translated, translation management systems (TMSs) often have the option to search for a match that is less than exact. Potential matches are provided to the translator as additional input for final translation. Translators who enhance their workflow with machine translation capabilities such as Amazon Translate often expect fuzzy matching data to be used as part of the automated translation solution.

In this post, you learn how to customize output from Amazon Translate according to translation memory fuzzy match quality scores.

Translation Quality Match

The XML Localization Interchange File Format (XLIFF) standard is often used as a data exchange format between TMSs and Amazon Translate. XLIFF files produced by TMSs include source and target text data along with match quality scores based on the available TM. These scores—usually expressed as a percentage—indicate how close the translation memory is to the text being translated.

Some customers with very strict requirements only want machine translation to be used when match quality scores are below a certain threshold. Beyond this threshold, they expect their own translation memory to take precedence. Translators often need to apply these preferences manually either within their TMS or by altering the text data. This flow is illustrated in the following diagram. The machine translation system processes the translation data—text and fuzzy match scores— which is then reviewed and manually edited by translators, based on their desired quality thresholds. Applying thresholds as part of the machine translation step allows you to remove these manual steps, which improves efficiency and optimizes cost.

Machine Translation Review Flow

Figure 1: Machine Translation Review Flow

The solution presented in this post allows you to enforce rules based on match quality score thresholds to drive whether a given input text should be machine translated by Amazon Translate or not. When not machine translated, the resulting text is left to the discretion of the translators reviewing the final output.

Solution Architecture

The solution architecture illustrated in Figure 2 leverages the following services:

  • Amazon Simple Storage Service – Amazon S3 buckets contain the following content:
    • Fuzzy match threshold configuration files
    • Source text to be translated
    • Amazon Translate input and output data locations
  • AWS Systems Manager – We use Parameter Store parameters to store match quality threshold configuration values
  • AWS Lambda – We use two Lambda functions:
    • One function preprocesses the quality match threshold configuration files and persists the data into Parameter Store
    • One function automatically creates the asynchronous translation jobs
  • Amazon Simple Queue Service – An Amazon SQS queue triggers the translation flow as a result of new files coming into the source bucket

Solution Architecture Diagram

Figure 2: Solution Architecture

You first set up quality thresholds for your translation jobs by editing a configuration file and uploading it into the fuzzy match threshold configuration S3 bucket. The following is a sample configuration in CSV format. We chose CSV for simplicity, although you can use any format. Each line represents a threshold to be applied to either a specific translation job or as a default value to any job.

default, 75 SourceMT-Test, 80

The specifications of the configuration file are as follows:

  • Column 1 should be populated with the name of the XLIFF file—without extension—provided to the Amazon Translate job as input data.
  • Column 2 should be populated with the quality match percentage threshold. For any score below this value, machine translation is used.
  • For all XLIFF files whose name doesn’t match any name listed in the configuration file, the default threshold is used—the line with the keyword default set in Column 1.

Auto-generated parameter in Systems Manager Parameter Store

Figure 3: Auto-generated parameter in Systems Manager Parameter Store

When a new file is uploaded, Amazon S3 triggers the Lambda function in charge of processing the parameters. This function reads and stores the threshold parameters into Parameter Store for future usage. Using Parameter Store avoids performing redundant Amazon S3 GET requests each time a new translation job is initiated. The sample configuration file produces the parameter tags shown in the following screenshot.

The job initialization Lambda function uses these parameters to preprocess the data prior to invoking Amazon Translate. We use an English-to-Spanish translation XLIFF input file, as shown in the following code. It contains the initial text to be translated, broken down into what is referred to as segments, represented in the source tags.

Consent Form CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Screening Visit Selección

The source text has been pre-matched with the translation memory beforehand. The data contains potential translation alternatives—represented as tags—alongside a match quality attribute, expressed as a percentage. The business rule is as follows:

  • Segments received with alternative translations and a match quality below the threshold are untouched or empty. This signals to Amazon Translate that they must be translated.
  • Segments received with alternative translations with a match quality above the threshold are pre-populated with the suggested target text. Amazon Translate skips those segments.

Let’s assume the quality match threshold configured for this job is 80%. The first segment with 99% match quality isn’t machine translated, whereas the second segment is, because its match quality is below the defined threshold. In this configuration, Amazon Translate produces the following output:

Consent Form FORMULARIO DE CONSENTIMIENTO CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Visita de selección Screening Visit Selección

In the second segment, Amazon Translate overwrites the target text initially suggested (Selección) with a higher quality translation: Visita de selección.

One possible extension to this use case could be to reuse the translated output and create our own translation memory. Amazon Translate supports customization of machine translation using translation memory thanks to the parallel data feature. Text segments previously machine translated due to their initial low-quality score could then be reused in new translation projects.

In the following sections, we walk you through the process of deploying and testing this solution. You use AWS CloudFormation scripts and data samples to launch an asynchronous translation job personalized with a configurable quality match threshold.

Prerequisites

For this walkthrough, you must have an AWS account. If you don’t have an account yet, you can create and activate one.

Launch AWS CloudFormation stack

  1. Choose Launch Stack:
  2. For Stack name, enter a name.
  3. For ConfigBucketName, enter the S3 bucket containing the threshold configuration files.
  4. For ParameterStoreRoot, enter the root path of the parameters created by the parameters processing Lambda function.
  5. For QueueName, enter the SQS queue that you create to post new file notifications from the source bucket to the job initialization Lambda function. This is the function that reads the configuration file.
  6. For SourceBucketName, enter the S3 bucket containing the XLIFF files to be translated. If you prefer to use a preexisting bucket, you need to change the value of the CreateSourceBucket parameter to No.
  7. For WorkingBucketName, enter the S3 bucket Amazon Translate uses for input and output data.
  8. Choose Next.

    Figure 4: CloudFormation stack details

  9. Optionally on the Stack Options page, add key names and values for the tags you may want to assign to the resources about to be created.
  10. Choose Next.
  11. On the Review page, select I acknowledge that this template might cause AWS CloudFormation to create IAM resources.
  12. Review the other settings, then choose Create stack.

AWS CloudFormation takes several minutes to create the resources on your behalf. You can watch the progress on the Events tab on the AWS CloudFormation console. When the stack has been created, you can see a CREATE_COMPLETE message in the Status column on the Overview tab.

Test the solution

Let’s go through a simple example.

  1. Download the following sample data.
  2. Unzip the content.

There should be two files: an .xlf file in XLIFF format, and a threshold configuration file with .cfg as the extension. The following is an excerpt of the XLIFF file.

English to French sample file extract

Figure 5: English to French sample file extract

  1. On the Amazon S3 console, upload the quality threshold configuration file into the configuration bucket you specified earlier.

The value set for test_En_to_Fr is 75%. You should be able to see the parameters on the Systems Manager console in the Parameter Store section.

  1. Still on the Amazon S3 console, upload the .xlf file into the S3 bucket you configured as source. Make sure the file is under a folder named translate (for example, /translate/test_En_to_Fr.xlf).

This starts the translation flow.

  1. Open the Amazon Translate console.

A new job should appear with a status of In Progress.

Auto-generated parameter in Systems Manager Parameter Store

Figure 6: In progress translation jobs on Amazon Translate console

  1. Once the job is complete, click into the job’s link and consult the output. All segments should have been translated.

All segments should have been translated. In the translated XLIFF file, look for segments with additional attributes named lscustom:match-quality, as shown in the following screenshot. These custom attributes identify segments where suggested translation was retained based on score.

Custom attributes identifying segments where suggested translation was retained based on score

Figure 7: Custom attributes identifying segments where suggested translation was retained based on score

These were derived from the translation memory according to the quality threshold. All other segments were machine translated.

You have now deployed and tested an automated asynchronous translation job assistant that enforces configurable translation memory match quality thresholds. Great job!

Cleanup

If you deployed the solution into your account, don’t forget to delete the CloudFormation stack to avoid any unexpected cost. You need to empty the S3 buckets manually beforehand.

Conclusion

In this post, you learned how to customize your Amazon Translate translation jobs based on standard XLIFF fuzzy matching quality metrics. With this solution, you can greatly reduce the manual labor involved in reviewing machine translated text while also optimizing your usage of Amazon Translate. You can also extend the solution with data ingestion automation and workflow orchestration capabilities, as described in Speed Up Translation Jobs with a Fully Automated Translation System Assistant.

About the Authors

Narcisse Zekpa is a Solutions Architect based in Boston. He helps customers in the Northeast U.S. accelerate their adoption of the AWS Cloud, by providing architectural guidelines, design innovative, and scalable solutions. When Narcisse is not building, he enjoys spending time with his family, traveling, cooking, and playing basketball.

Dimitri Restaino is a Solutions Architect at AWS, based out of Brooklyn, New York. He works primarily with Healthcare and Financial Services companies in the North East, helping to design innovative and creative solutions to best serve their customers. Coming from a software development background, he is excited by the new possibilities that serverless technology can bring to the world. Outside of work, he loves to hike and explore the NYC food scene.



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Enhance the caller experience with hints in Amazon Lex

We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up…

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We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up such clues or hints based on their domain knowledge and access to business intelligence dashboards. Amazon Lex now supports a hints capability to enhance the recognition of relevant phrases in a conversation. You can programmatically provide phrases as hints during a live interaction to influence the transcription of spoken input. Better recognition drives efficient conversations, reduces agent handling time, and ultimately increases customer satisfaction.

In this post, we review the runtime hints capability and use it to implement verification of callers based on their mother’s maiden name.

Overview of the runtime hints capability

You can provide a list of phrases or words to help your bot with the transcription of speech input. You can use these hints with built-in slot types such as first and last names, street names, city, state, and country. You can also configure these for your custom slot types.

You can use the capability to transcribe names that may be difficult to pronounce or understand. For example, in the following sample conversation, we use it to transcribe the name “Loreck.”

Conversation 1

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 1111 2222 3333 4444

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Loreck

IVR: Thank you. The balance on your checking account is 123 dollars.

Words provided as hints are preferred over other similar words. For example, in the second sample conversation, the runtime hint (“Smythe”) is selected over a more common transcription (“Smith”).

Conversation 2

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Smythe

IVR: Thank you. The balance on your checking account is 456 dollars.

If the name doesn’t match the runtime hint, you can fail the verification and route the call to an agent.

Conversation 3

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Savings

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Jane

IVR: There is an issue with your account. For support, you will be forwarded to an agent.

Solution overview

Let’s review the overall architecture for the solution (see the following diagram):

  • We use an Amazon Lex bot integrated with an Amazon Connect contact flow to deliver the conversational experience.
  • We use a dialog codehook in the Amazon Lex bot to invoke an AWS Lambda function that provides the runtime hint at the previous turn of the conversation.
  • For the purposes of this post, the mother’s maiden name data used for authentication is stored in an Amazon DynamoDB table.
  • After the caller is authenticated, the control is passed to the bot to perform transactions (for example, check balance)

In addition to the Lambda function, you can also send runtime hints to Amazon Lex V2 using the PutSession, RecognizeText, RecognizeUtterance, or StartConversation operations. The runtime hints can be set at any point in the conversation and are persisted at every turn until cleared.

Deploy the sample Amazon Lex bot

To create the sample bot and configure the runtime phrase hints, perform the following steps. This creates an Amazon Lex bot called BankingBot, and one slot type (accountNumber).

  1. Download the Amazon Lex bot.
  2. On the Amazon Lex console, choose Actions, Import.
  3. Choose the file BankingBot.zip that you downloaded, and choose Import.
  4. Choose the bot BankingBot on the Amazon Lex console.
  5. Choose the language English (GB).
  6. Choose Build.
  7. Download the supporting Lambda code.
  8. On the Lambda console, create a new function and select Author from scratch.
  9. For Function name, enter BankingBotEnglish.
  10. For Runtime, choose Python 3.8.
  11. Choose Create function.
  12. In the Code source section, open lambda_function.py and delete the existing code.
  13. Download the function code and open it in a text editor.
  14. Copy the code and enter it into the empty function code field.
  15. Choose deploy.
  16. On the Amazon Lex console, select the bot BankingBot.
  17. Choose Deployment and then Aliases, then choose the alias TestBotAlias.
  18. On the Aliases page, choose Languages and choose English (GB).
  19. For Source, select the bot BankingBotEnglish.
  20. For Lambda version or alias, enter $LATEST.
  21. On the DynamoDB console, choose Create table.
  22. Provide the name as customerDatabase.
  23. Provide the partition key as accountNumber.
  24. Add an item with accountNumber: “1111222233334444” and mothersMaidenName “Loreck”.
  25. Add item with accountNumber: “5555666677778888” and mothersMaidenName “Smythe”.
  26. Make sure the Lambda function has permissions to read from the DynamoDB table customerDatabase.
  27. On the Amazon Connect console, choose Contact flows.
  28. In the Amazon Lex section, select your Amazon Lex bot and make it available for use in the Amazon Connect contact flow.
  29. Download the contact flow to integrate with the Amazon Lex bot.
  30. Choose the contact flow to load it into the application.
  31. Make sure the right bot is configured in the “Get Customer Input” block.
  32. Choose a queue in the “Set working queue” block.
  33. Add a phone number to the contact flow.
  34. Test the IVR flow by calling in to the phone number.

Test the solution

You can now call in to the Amazon Connect phone number and interact with the bot.

Conclusion

Runtime hints allow you to influence the transcription of words or phrases dynamically in the conversation. You can use business logic to identify the hints as the conversation evolves. Better recognition of the user input allows you to deliver an enhanced experience. You can configure runtime hints via the Lex V2 SDK. The capability is available in all AWS Regions where Amazon Lex operates in the English (Australia), English (UK), and English (US) locales.

To learn more, refer to runtime hints.

About the Authors

Kai Loreck is a professional services Amazon Connect consultant. He works on designing and implementing scalable customer experience solutions. In his spare time, he can be found playing sports, snowboarding, or hiking in the mountains.

Anubhav Mishra is a Product Manager with AWS. He spends his time understanding customers and designing product experiences to address their business challenges.

Sravan Bodapati is an Applied Science Manager at AWS Lex. He focuses on building cutting edge Artificial Intelligence and Machine Learning solutions for AWS customers in ASR and NLP space. In his spare time, he enjoys hiking, learning economics, watching TV shows and spending time with his family.



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