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Serve 3,000 deep learning models on Amazon EKS with AWS Inferentia for under $50 an hour

More customers are finding the need to build larger, scalable, and more cost-effective machine learning (ML) inference pipelines in the cloud. Outside of these base prerequisites, the requirements of ML inference pipelines in production vary based on the business use case. A typical inference architecture for applications like recommendation engines, sentiment analysis, and ad ranking…

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More customers are finding the need to build larger, scalable, and more cost-effective machine learning (ML) inference pipelines in the cloud. Outside of these base prerequisites, the requirements of ML inference pipelines in production vary based on the business use case. A typical inference architecture for applications like recommendation engines, sentiment analysis, and ad ranking need to serve a large number of models, with a mix of classical ML and deep learning (DL) models. Each model has to be accessible through an application programing interface (API) endpoint and be able to respond within a predefined latency budget from the time it receives a request.

In this post, we describe an inference architecture, developed in collaboration with the Commerce Einstein Team at Salesforce, built on Amazon Elastic Kubernetes Service (Amazon EKS) to not only address the base prerequisites, but also pack thousands of unique DL models in a scalable architecture. We explore a mix of Amazon Elastic Compute Cloud (Amazon EC2) instance families (c5, g4dn, Inf1) to develop an optimal design from a cost and performance aspect. To meet these requirements, we build the DL inferencing service on Amazon EKS using FastAPI, a lightweight and efficient Python-based API server, and develop a model bin packing strategy to efficiently share compute and memory resources between models. To load test the architecture, we use a natural language processing (NLP) open-source model from huggingface.co (bert-base-cased, approximately 800 MB) and simulate thousands of clients sending simultaneous requests to the service pool. We use AWS Inferentia, the custom ML chip on Inf1 instances, to package and serve 3,000 unique ML models while keeping the cost below $50/hour (On-Demand pricing), with a round trip latency of 70 milliseconds (P90) versus our target of 100 milliseconds. You can extend this architecture and optimization approach to any custom DL model.

Solution overview

The following is a simple, scalable, and highly available architecture based on a standard Amazon EKS infrastructure that can be deployed across Availability Zones.

The Amazon EKS cluster has several node groups, with one EC2 instance family per node group. Each node group can support different instance types, such as CPU (c5), GPU (g4dn), and AWS Inferentia (Inf1), and can pack multiple models per instance to maximize the number of served models. Next, the model serving application and DL framework dependencies are containerized, and these container images are stored on Amazon Elastic Container Registry (Amazon ECR). The container images are deployed to the cluster using deployment and service manifests, customized for each instance family. The model serving application downloads the model artifacts from Amazon Simple Storage Service (Amazon S3) at server initialization, which reduces the size of the container images on Amazon ECR and decouples the model data from the service definition.

Services like cluster-autoscaler, horizontal-pod-autoscaler, aws-load-balancer-controller, metrics-server, nginx-ingress-controller, neuron-device-plugin-daemonset, and nvidia-device-plugin-daemonset are deployed to the cluster as required. This design relies on Kubernetes coredns for name resolution of the service endpoints within the cluster. Each model is addressable through a combination of DNS name and model name. For example, http://..svc.cluster.local:8080/predictions/.

For customization of the DNS names, we can use an ingress manifest.

The architecture is set up to run in five simple steps: build, trace, pack, deploy, and test. The code repository can be accessed in the GitHub repo.

  1. The build step builds the base container for the selected instance type, installing all the necessary base layers in the container.
  2. The trace step compiles the model. The model runs on the target EC2 instance. To run on Inf1, you need to use the AWS Neuron SDK to trace the model. The following is the code snippet to trace a model that runs in bfloat16 on AWS Inferentia or Automatic Mixed Precision (AMP) on a GPU instance:

print(‘nTracing model …’) example_inputs = ( torch.cat([inputs[‘input_ids’]] * batch_size,0), torch.cat([inputs[‘attention_mask’]] * batch_size,0) ) os.makedirs(f’traced-{model_name}’, exist_ok=True) torch.set_num_threads(6) if ‘inf’ in processor: model_traced = torch.neuron.trace(model, example_inputs, verbose=1, compiler_workdir=f’./traced-{model_name}/compile_wd_{processor}_bs{batch_size}_seq {sequence_length}_pc{pipeline_cores}’, compiler_args = [‘–neuroncore-pipeline-cores’, str(pipeline_cores)]) else: model_traced = torch.jit.trace(model, example_inputs)

  1. The pack step packs the model in a container with FastAPI, which also allows packing multiple models within the same container.
  2. The deploy step runs the model in the configured runtime (such as Kubernetes or Docker) and facilitates the management of the full lifecycle of the model server containers. The following code snippet sets the run options and launches the container in a configured runtime:

echo “Runtime: $runtime” echo “Processor: $processor” if [ “$runtime” == “docker” ]; then server=0 while [ $server -lt $num_servers ]; do run_opts=”–name ${app_name}-${server} -e NUM_MODELS=$num_models -e POSTPROCESS=$postprocess -e QUIET=$quiet -P” if [ “$processor” == “gpu” ]; then run_opts=”–gpus 0 ${run_opts}” fi CMD=”docker run -d ${run_opts} ${registry}${model_image_name}${model_image_tag}” echo “$CMD” eval “$CMD” server=$((server+1)) done elif [ “$runtime” == “kubernetes” ]; then kubectl create namespace ${namespace} –dry-run=client -o yaml | kubectl apply -f – ./generate-yaml.sh kubectl apply -f ${app_dir} else echo “Runtime $runtime not recognized” fi

  1. The final step runs tests against the model servers deployed in the runtime environment.

Bin packing ML models

Bin packing of ML models across EC2 instances is essential to efficiently share compute and memory resources between models. The task of bin packing the models can be formulated and solved as a 0-1 knapsack problem using combinatorial optimization. The following is the mathematical formulation for bin packing; nmodels is subject to a constraint of max processor memory (Mmax) and utilization (Cmax). This approach recommends optimal bin packing of the models across a minimal set of EC2 instances.

The number of bins is supersized to initialize the problem with a trivial solution of one model per bin and pruned to attain the target with minimal number of bins.

The following visualization shows a sample bin packing allocation for 78 models, each with unique memory and compute requirements. In this example, 78 models were packed into 23 instances (g4dn, Inf1) with a specified target maximum memory (7.4 GB) and compute capacity (83%). The coloring legend indicates model index.

We can use the preceding method to optimally bin pack models across instances. The following table summarizes bin packing results for Inf1 and g4dn instances. We chose these instance families because the transformer-based NLP model requires hardware acceleration to achieve the expected latency. We were able to bin pack more models in Inf1.6xlarge (48 GiB memory) compared to g4dn.12xlarge (192 GiB memory) because the Neuron compiler’s Auto Casting capability automatically converts FP32 models to 16-bit bfloat to maximize throughput.

Model EC2 Instance Server Type Number of Models Bin Packed per Instance Price per Instance
(On-Demand), $/hr
Price per Model-hour
($)
bert-base-cased inf1.2xlarge FastAPI 24 0.362 0.015
bert-base-cased g4dn.xlarge FastAPI 18 0.526 0.029
bert-base-cased inf1.xlarge FastAPI 11 0.228 0.020
bert-base-cased inf1.6xlarge FastAPI 76 1.180 0.015
bert-base-cased g4dn.12xlarge FastAPI 72 3.912 0.054

Test methodology and observations

To load test at scale, we simulated over 40 clients sending simultaneous requests to the test pool (loading from multiple clients). Loading the system with more user requests increases the throughput at the expense of latency; the tests were designed to sweep the throughput-latency curve to find data points with optimized resource usage. We measured throughput and latency (P50, P90, P95) and summarized the results across four metrics: latency, throughput (inferences per second), number of models served per instance, and cost. In addition, we created tests to simulate single-sequential and single-random requests using curl to send a GET or POST request to each model in the test pool. The purpose of these experiments was to measure the best-case latency we can expect, as a baseline.

We ran several experiments to find the optimal model packing across minimal sets of EC2 instances that would result in the highest performance at the lowest cost. The following table summarizes some of these test results. The best results were observed upon using 40 Inf1.6xl instances with DNS caching enabled, serving 3,040 models with a throughput of 6,230 requests per second in 66 milliseconds (P90 latency) at a cost of $47.2/hour (On-Demand). The best mixed instance deployment, using 32 inf1.6xl and 21 g4dn.12xl instances, resulted in 3,048 models served, but with much lower throughput at 248 requests per second with an increased hourly cost of $119.91/hour. Although we didn’t use EC2 Spot Instances as a lever for cost-optimization in this architecture, we highly recommend using Spot if your inference workload is time flexible and fault tolerant. The following table summarizes our observations across instance types.

Exp. # Instances
(num x type)
Models (num) Sequential Response (ms) Random Response (ms) Throughput (req/s) Latency with Load P90 (ms) On-Demand Cost ($/hr)
1 3 x inf1.6xl 144 21 – 32 21 – 30 142 57 3.54
2 5 x inf1.6xl 240 23 – 35 21 – 35 173 56 5.9
3 21 x inf1.6xl 1008 23 – 39 23 – 35 218 24 24.78
4 32 x inf1.6xl 1536 26 – 33 25 – 37 217 23 37.76
5 4 x g4dn.12xl 288 27 – 34 28 – 37 178 30 15.64
6 14 x g4dn.12xl 1008 26 – 35 31 – 41 154 30 54.76
7 32 x inf1.6xl +
21 x g4dn.12xl
3048 27 – 35 24 – 45 248 28 119.91
8 40 x inf1.6xl 3002 24 – 31 25 – 38 1536 33 47.2
9 40 x inf1.6xl
(With DNS Caching)
3040 24 – 31 25 – 38 6230 66 47.2

Conclusion

With this scalable architecture, we were able to scale inference across 3,000 models, achieving a target latency of 100 milliseconds, while simultaneously keeping costs under $50/hour (On-Demand) by optimizing for cost by efficiently bin packing models across a minimal set of EC2 instances. Across all the tests, Inf1 instances yielded the highest throughput, lowest cost, fastest response time, and the maximum bin packing ratio compared to other instances. AWS customers like Snap, Airbnb, Sprinklr and many more have been using AWS Inferentia to achieve the highest performance and lowest cost on a variety of deployments. Although the DL model tested requires the use of hardware acceleration on Inf1 and g4dn instance types, you can match other model types with different instance types (Inf1, CPU, GPU) and bin pack models accordingly by using the methodology described.

Learn more about the AWS Inferentia chip and EC2 Inf1 instances to get started running your own custom ML pipelines on AWS Inferentia using the Neuron SDK.

About the Authors

Alex Iankoulski is a Principal Solutions Architect with a focus on autonomous workloads using containers. Alex is a hands-on full-stack infrastructure and software architect and has been building platforms using Docker to help accelerate the pace of innovation by applying container technologies to engineering, data science, and AI problems. Over the past 10 years, he has worked on combating climate change, democratizing AI and ML, and making travel safer, healthcare better, and energy smarter.

Mahadevan Balasubramaniam is a Principal Solutions Architect for Autonomous Computing with nearly 20 years of experience in the area of physics-infused deep learning, building and deploying digital twins for industrial systems at scale. Mahadevan obtained his PhD in Mechanical Engineering from Massachusetts Institute of Technology and has over 25 patents and publications to his credit.

Sundar Ranganathan is the Head of Business Development, ML Frameworks on the Amazon EC2 team. He focuses on large-scale ML workloads across AWS services like Amazon EKS, Amazon ECS, Elastic Fabric Adapter, AWS Batch, and Amazon SageMaker. His experience includes leadership roles in product management and product development at NetApp, Micron Technology, Qualcomm, and Mentor Graphics.

Joshua Correa is a Salesforce Principal Member of Technical Staff working on the Commerce Einstein Team. Joshua has deep passion for building scalable, resilient and cost effective infrastructure for Machine Learning. Joshua enjoys working at the intersection of software engineering and data science to bring cutting edge models into production.



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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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