Monthly Archives: July 2022

Imagine a surgeon taking video calls with patients across the globe without the need of a human translator. What if a fledgling startup could easily expand their product across borders and into new geographical markets by offering fluid, accurate, multilingual customer support and sales, all without the need of a live human translator? What happens to your business when you’re no longer bound by language?

It’s common today to have virtual meetings with international teams and customers that speak many different languages. Whether they’re internal or external meetings, meaning often gets lost in complex discussions and you may encounter language barriers that prevent you from being as effective as you could be.

In this post, you will learn how to use three fully managed AWS services (Amazon Transcribe, Amazon Translate, and Amazon Polly) to produce a near-real-time speech-to-speech translator solution that can quickly translate a source speaker’s live voice input into a spoken, accurate, translated target language, all with zero machine learning (ML) experience.

Overview of solution

Our translator consists of three fully managed AWS ML services working together in a single Python script by using the AWS SDK for Python (Boto3) for our text translation and text-to-speech portions, and an asynchronous streaming SDK for audio input transcription.

Amazon Transcribe: Streaming speech to text

The first service you use in our stack is Amazon Transcribe, a fully managed speech-to-text service that takes input speech and transcribes it to text. Amazon Transcribe has flexible ingestion methods, batch or streaming, because it accepts either stored audio files or streaming audio data. In this post, you use the asynchronous Amazon Transcribe streaming SDK for Python, which uses the HTTP/2 streaming protocol to stream live audio and receive live transcriptions.

When we first built this prototype, Amazon Transcribe streaming ingestion didn’t support automatic language detection, but this is no longer the case as of November 2021. Both batch and streaming ingestion now support automatic language detection for all supported languages. In this post, we show how a parameter-based solution though a seamless multi-language parameterless design is possible through the use of streaming automatic language detection. After our transcribed speech segment is returned as text, you send a request to Amazon Translate to translate and return the results in our Amazon Transcribe EventHandler method.

Amazon Translate: State-of-the-art, fully managed translation API

Next in our stack is Amazon Translate, a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation. As of June of 2022, Amazon Translate supports translation across 75 languages, with new language pairs and improvements being made constantly. Amazon Translate uses deep learning models hosted on a highly scalable and resilient AWS Cloud architecture to quickly deliver accurate translations either in real time or batched, depending on your use case. Using Amazon Translate is straightforward and requires no management of underlying architecture or ML skills. Amazon Translate has several features, like creating and using a custom terminology to handle mapping between industry-specific terms. For more information on Amazon Translate service limits, refer to Guidelines and limits. After the application receives the translated text in our target language, it sends the translated text to Amazon Polly for immediate translated audio playback.

Amazon Polly: Fully managed text-to-speech API

Finally, you send the translated text to Amazon Polly, a fully managed text-to-speech service that can either send back lifelike audio clip responses for immediate streaming playback or batched and saved in Amazon Simple Storage Service (Amazon S3) for later use. You can control various aspects of speech such as pronunciation, volume, pitch, speech rate, and more using standardized Speech Synthesis Markup Language (SSML).

You can synthesize speech for certain Amazon Polly Neural voices using the Newscaster style to make them sound like a TV or radio newscaster. You can also detect when specific words or sentences in the text are being spoken based on the metadata included in the audio stream. This allows the developer to synchronize graphical highlighting and animations, such as the lip movements of an avatar, with the synthesized speech.

You can modify the pronunciation of particular words, such as company names, acronyms, foreign words, or neologisms, for example “P!nk,” “ROTFL,” or “C’est la vie” (when spoken in a non-French voice), using custom lexicons.

Architecture overview

The following diagram illustrates our solution architecture.

This diagram shows the data flow from the client device to Amazon Transcribe, Amazon Translate, and Amazon Polly

The workflow is as follows:

1. Audio is ingested by the Python SDK.
2. Amazon Polly converts the speech to text, in 39 possible languages.
3. Amazon Translate converts the languages.
4. Amazon Live Transcribe converts text to speech.
5. Audio is outputted to speakers.

Prerequisites

You need a host machine set up with a microphone, speakers, and reliable internet connection. A modern laptop should work fine for this because no additional hardware is needed. Next, you need to set up the machine with some software tools.

You must have Python 3.7+ installed to use the asynchronous Amazon Transcribe streaming SDK and for a Python module called pyaudio, which you use to control the machine’s microphone and speakers. This module depends on a C library called portaudio.h. If you encounter issues with pyaudio errors, we suggest checking your OS to see if you have the portaudio.h library installed.

For authorization and authentication of service calls, you create an AWS Identity and Access Management (IAM) service role with permissions to call the necessary AWS services. By configuring the AWS Command Line Interface (AWS CLI) with this IAM service role, you can run our script on your machine without having to pass in keys or passwords, because the AWS libraries are written to use the configured AWS CLI user’s credentials. This is a convenient method for rapid prototyping and ensures our services are being called by an authorized identity. As always, follow the principle of least privilege when assigning IAM policies when creating an IAM user or role.

To summarize, you need the following prerequisites:

• A PC, Mac, or Linux machine with microphone, speakers, and internet connection
• The portaudio.h C library for your OS (brew, apt get, wget), which is needed for pyaudio to work
• AWS CLI 2.0 with properly authorized IAM user configured by running aws configure in the AWS CLI
• Python 3.7+
• The asynchronous Amazon Transcribe Python SDK
• The following Python libraries:
  • boto3
  • amazon-transcribe
  • pyaudio
  • asyncio
  • concurrent

Implement the solution

You will be relying heavily on the asynchronous Amazon Transcribe streaming SDK for Python as a starting point, and are going to build on top of that specific SDK. After you have experimented with the streaming SDK for Python, you add streaming microphone input by using pyaudio, a commonly used Python open-source library used for manipulating audio data. Then you add Boto3 calls to Amazon Translate and Amazon Polly for our translation and text-to-speech functionality. Finally, you stream out translated speech through the computer’s speakers again with pyaudio. The Python module concurrent gives you the ability to run blocking code in its own asynchronous thread to play back your returned Amazon Polly speech in a seamless, non-blocking way.

Let’s import all our necessary modules, transcribe streaming classes, and instantiate some globals:

import boto3
 import asyncio
 import pyaudio
 import concurrent
 from amazon_transcribe.client import TranscribeStreamingClient
 from amazon_transcribe.handlers import TranscriptResultStreamHandler
 from amazon_transcribe.model import TranscriptEvent


 polly = boto3.client('polly', region_name = 'us-west-2')
 translate = boto3.client(service_name='translate', region_name='us-west-2', use_ssl=True)
 pa = pyaudio.PyAudio()

 #for mic stream, 1024 should work fine
 default_frames = 1024

 #current params are set up for English to Mandarin, modify to your liking
 params['source_language'] = "en"
 params['target_language'] = "zh"
 params['lang_code_for_polly'] = "cmn-CN"
 params['voice_id'] = "Zhiyu"
 params['lang_code_for_transcribe'] = "en-US"

First, you use pyaudio to obtain the input device’s sampling rate, device index, and channel count:

#try grabbing the default input device and see if we get lucky
 default_indput_device = pa.get_default_input_device_info()

 # verify this is your microphone device 
 print(default_input_device)

 #if correct then set it as your input device and define some globals
 input_device = default_input_device

 input_channel_count = input_device["maxInputChannels"]
 input_sample_rate = input_device["defaultSampleRate"]
 input_dev_index = input_device["index"]

If this isn’t working, you can also loop through and print your devices as shown in the following code, and then use the device index to retrieve the device information with pyaudio:

print ("Available devices:n")
 for i in range(0, pa.get_device_count()):
     info = pa.get_device_info_by_index(i)
     print (str(info["index"])  + ": t %s n t %s n" % (info["name"], p.get_host_api_info_by_index(info["hostApi"])["name"]))

 # select the correct index from the above returned list of devices, for example zero
 dev_index = 0 
 input_device = pa.get_device_info_by_index(dev_index)

 #set globals for microphone stream
 input_channel_count = input_device["maxInputChannels"]
 input_sample_rate = input_device["defaultSampleRate"]
 input_dev_index = input_device["index"]

You use channel_count, sample_rate, and dev_index as parameters in a mic stream. In that stream’s callback function, you use an asyncio nonblocking thread-safe callback to put the input bytes of the mic stream into an asyncio input queue. Take note of the loop and input_queue objects created with asyncio and how they’re used in the following code:

async def mic_stream():
     # This function wraps the raw input stream from the microphone forwarding
     # the blocks to an asyncio.Queue.
     
     loop = asyncio.get_event_loop()
     input_queue = asyncio.Queue()
     
     def callback(indata, frame_count, time_info, status):
         loop.call_soon_threadsafe(input_queue.put_nowait, indata)
         return (indata, pyaudio.paContinue)
         
     # Be sure to use the correct parameters for the audio stream that matches
     # the audio formats described for the source language you'll be using:
     # https://docs.aws.amazon.com/transcribe/latest/dg/streaming.html
     
     print(input_device)
     
     #Open stream
     stream = pa.open(format = pyaudio.paInt16,
                 channels = input_channel_count,
                 rate = int(input_sample_rate),
                 input = True,
                 frames_per_buffer = default_frames,
                 input_device_index = input_dev_index,
                 stream_callback=callback)
     # Initiate the audio stream and asynchronously yield the audio chunks
     # as they become available.
     stream.start_stream()
     print("started stream")
     while True:
         indata = await input_queue.get()
         yield indata

Now when the generator function mic_stream() is called, it continually yields input bytes as long as there is microphone input data in the input queue.

Now that you know how to get input bytes from the microphone, let’s look at how to write Amazon Polly output audio bytes to a speaker output stream:

#text will come from MyEventsHandler
 def aws_polly_tts(text):

     response = polly.synthesize_speech(
         Engine = 'standard',
         LanguageCode = params['lang_code_for_polly'],
         Text=text,
         VoiceId = params['voice_id'],
         OutputFormat = "pcm",
     )
     output_bytes = response['AudioStream']
     
     #play to the speakers
     write_to_speaker_stream(output_bytes)
     
 #how to write audio bytes to speakers

 def write_to_speaker_stream(output_bytes):
     """Consumes bytes in chunks to produce the response's output'"""
     print("Streaming started...")
     chunk_len = 1024
     channels = 1
     sample_rate = 16000
     
     if output_bytes:
         polly_stream = pa.open(
                     format = pyaudio.paInt16,
                     channels = channels,
                     rate = sample_rate,
                     output = True,
                     )
         #this is a blocking call - will sort this out with concurrent later
         while True:
             data = output_bytes.read(chunk_len)
             polly_stream.write(data)
             
         #If there's no more data to read, stop streaming
             if not data:
                 output_bytes.close()
                 polly_stream.stop_stream()
                 polly_stream.close()
                 break
         print("Streaming completed.")
     else:
         print("Nothing to stream.")

Now let’s expand on what you built in the post Asynchronous Amazon Transcribe Streaming SDK for Python. In the following code, you create an executor object using the ThreadPoolExecutor subclass with three workers with concurrent. You then add an Amazon Translate call on the finalized returned transcript in the EventHandler and pass that translated text, the executor object, and our aws_polly_tts() function into an asyncio loop with loop.run_in_executor(), which runs our Amazon Polly function (with translated input text) asynchronously at the start of next iteration of the asyncio loop.

#use concurrent package to create an executor object with 3 workers ie threads
 executor = concurrent.futures.ThreadPoolExecutor(max_workers=3)

 class MyEventHandler(TranscriptResultStreamHandler):
     async def handle_transcript_event(self, transcript_event: TranscriptEvent):

         #If the transcription is finalized, send it to translate
 
         results = transcript_event.transcript.results
         if len(results) > 0:
             if len(results[0].alternatives) > 0:
                 transcript = results[0].alternatives[0].transcript
                 print("transcript:", transcript)

                 print(results[0].channel_id)
                 if hasattr(results[0], "is_partial") and results[0].is_partial == False:
                     
                     #translate only 1 channel. the other channel is a duplicate
                     if results[0].channel_id == "ch_0":
                         trans_result = translate.translate_text(
                             Text = transcript,
                             SourceLanguageCode = params['source_language'],
                             TargetLanguageCode = params['target_language']
                         )
                         print("translated text:" + trans_result.get("TranslatedText"))
                         text = trans_result.get("TranslatedText")

                         #we run aws_polly_tts with a non-blocking executor at every loop iteration
                         await loop.run_in_executor(executor, aws_polly_tts, text)  

Finally, we have the loop_me() function. In it, you define write_chunks(), which takes an Amazon Transcribe stream as an argument and asynchronously writes chunks of streaming mic input to it. You then use MyEventHandler() with the output transcription stream as its argument and create a handler object. Then you use await with asyncio.gather() and pass in the write_chunks() and handler with the handle_events() method to handle the eventual futures of these coroutines. Lastly, you gather all event loops and loop the loop_me() function with run_until_complete(). See the following code:

async def loop_me():
 # Setup up our client with our chosen AWS region

     client = TranscribeStreamingClient(region="us-west-2")
     stream = await client.start_stream_transcription(
         language_code=params['lang_code_for_transcribe'],
         media_sample_rate_hz=int(device_info["defaultSampleRate"]),
         number_of_channels = 2,
         enable_channel_identification=True,
         media_encoding="pcm",
     )
     recorded_frames = []
     async def write_chunks(stream):
         
         # This connects the raw audio chunks generator coming from the microphone
         # and passes them along to the transcription stream.
         print("getting mic stream")
         async for chunk in mic_stream():
             t.tic()
             recorded_frames.append(chunk)
             await stream.input_stream.send_audio_event(audio_chunk=chunk)
             t.toc("chunks passed to transcribe: ")
         await stream.input_stream.end_stream()

     handler = MyEventHandler(stream.output_stream)
     await asyncio.gather(write_chunks(stream), handler.handle_events())

 #write a proper while loop here
 loop = asyncio.get_event_loop()
 loop.run_until_complete(loop_me())
 loop.close()

When the preceding code is run together without errors, you can speak into the microphone and quickly hear your voice translated to Mandarin Chinese. The automatic language detection feature for Amazon Transcribe and Amazon Translate translates any supported input language into the target language. You can speak for quite some time and because of the non-blocking nature of the function calls, all your speech input is translated and spoken, making this an excellent tool for translating live speeches.

Conclusion

Although this post demonstrated how these three fully managed AWS APIs can function seamlessly together, we encourage you to think about how you could use these services in other ways to deliver multilingual support for services or media like multilingual closed captioning for a fraction of the current cost. Medicine, business, and even diplomatic relations could all benefit from an ever-improving, low-cost, low-maintenance translation service.

For more information about the proof of concept code base for this use case check out our Github.

About the Authors

Michael Tran is a Solutions Architect with Envision Engineering team at Amazon Web Services. He provides technical guidance and helps customers accelerate their ability to innovate through showing the art of the possible on AWS. He has built multiple prototypes around AI/ML, and IoT for our customers. You can contact me @Mike_Trann on Twitter.

Cameron Wilkes is a Prototyping Architect on the AWS Industry Accelerator team. While on the team he delivered several ML based prototypes to customers to demonstrate the “Art of the Possible” of ML on AWS. He enjoys music production, off-roading and design.

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If you use the default lifecycle configuration for your domain or user profile in Amazon SageMaker Studio and use Amazon SageMaker Data Wrangler for data preparation, then this post is for you. In this post, we show how you can create a Data Wrangler flow and use it for data preparation in a Studio environment with a default lifecycle configuration.

Data Wrangler is a capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications via a visual interface. Data preparation is a crucial step of the ML lifecycle, and Data Wrangler provides an end-to-end solution to import, explore, transform, featurize, and process data for ML in a visual, low-code experience. It lets you easily and quickly connect to AWS components like Amazon Simple Storage Service (Amazon S3), Amazon Athena, Amazon Redshift, and AWS Lake Formation, and external sources like Snowflake and DataBricks DeltaLake. Data Wrangler supports standard data types such as CSV, JSON, ORC, and Parquet.

Studio apps are interactive applications that enable Studio’s visual interface, code authoring, and run experience. App types can be either Jupyter Server or Kernel Gateway:

• Jupyter Server – Enables access to the visual interface for Studio. Every user in Studio gets their own Jupyter Server app.
• Kernel Gateway – Enables access to the code run environment and kernels for your Studio notebooks and terminals. For more information, see Jupyter Kernel Gateway.

Lifecycle configurations (LCCs) are shell scripts to automate customization for your Studio environments, such as installing JupyterLab extensions, preloading datasets, and setting up source code repositories. LCC scripts are triggered by Studio lifecycle events, such as starting a new Studio notebook. To set a lifecycle configuration as the default for your domain or user profile programmatically, you can create a new resource or update an existing resource. To associate a lifecycle configuration as a default, you first need to create a lifecycle configuration following the steps in Creating and Associating a Lifecycle Configuration

Note: Default lifecycle configurations set up at the domain level are inherited by all users, whereas those set up at the user level are scoped to a specific user. If you apply both domain-level and user profile-level lifecycle configurations at the same time, the user profile-level lifecycle configuration takes precedence and is applied to the application irrespective of what lifecycle configuration is applied at the domain level. For more information, see Setting Default Lifecycle Configurations.

Data Wrangler accepts the default Kernel Gateway lifecycle configuration, but some of the commands defined in the default Kernel Gateway lifecycle configuration aren’t applicable to Data Wrangler, which can cause Data Wrangler to fail to start. The following screenshot shows an example of an error message you might get when launching the Data Wrangler flow. This may happen only with default lifecycle configurations and not with lifecycle configurations.

Solution overview

Customers using the default lifecycle configuration in Studio can follow this post and use the supplied code block within the lifecycle configuration script to launch a Data Wrangler app without any errors.

Set up the default lifecycle configuration

To set up a default lifecycle configuration, you must add it to the DefaultResourceSpec of the appropriate app type. The behavior of your lifecycle configuration depends on whether it’s added to the DefaultResourceSpec of a Jupyter Server or Kernel Gateway app:

• Jupyter Server apps – When added to the DefaultResourceSpec of a Jupyter Server app, the default lifecycle configuration script runs automatically when the user logs in to Studio for the first time or restarts Studio. You can use this to automate one-time setup actions for the Studio developer environment, such as installing notebook extensions or setting up a GitHub repo. For an example of this, see Customize Amazon SageMaker Studio using Lifecycle Configurations.
• Kernel Gateway apps – When added to the DefaultResourceSpec of a Kernel Gateway app, Studio defaults to selecting the lifecycle configuration script from the Studio launcher. You can launch a notebook or terminal with the default script or choose a different one from the list of lifecycle configurations.

A default Kernel Gateway lifecycle configuration specified in DefaultResourceSpec applies to all Kernel Gateway images in the Studio domain unless you choose a different script from the list presented in the Studio launcher.

When you work with lifecycle configurations for Studio, you create a lifecycle configuration and attach it to either your Studio domain or user profile. You can then launch a Jupyter Server or Kernel Gateway application to use the lifecycle configuration.

The following table summarizes these errors you may encounter when launching a Data Wrangler application with default lifecycle configurations.

When you use the default lifecycle configuration associated with Studio and Data Wrangler (Kernel Gateway app), you might encounter Kernel Gateway app failure. In this post, we demonstrate how to set the default lifecycle configuration properly to exclude running commands in a Data Wrangler application so you don’t encounter Kernel Gateway app failure.

Let’s say you want to install a git-clone-repo script as the default lifecycle configuration that checks out a Git repository under the user’s home folder automatically when the Jupyter server starts. Let’s look at each scenario of applying a lifecycle configuration (Studio domain, user profile, or application level).

Apply lifecycle configuration at the Studio domain or user profile level

To apply the default Kernel Gateway lifecycle configuration at the Studio domain or user profile level, complete the steps in this section. We start with instructions for the user profile level.

In your lifecycle configuration script, you have to include the following code block that checks and skips the Data Wrangler Kernel Gateway app:

#!/bin/bash
set -eux
STATUS=$(
python3 -c "import sagemaker_dataprep"
echo $?
)
if [ "$STATUS" -eq 0 ]; then
echo 'Instance is of Type Data Wrangler'
else
echo 'Instance is not of Type Data Wrangler'
<remainder of LCC here within in else block – this contains some pip install, etc>
fi

For example, let’s use the following script as our original (note that the folder to clone the repo is changed to /root from /home/sagemaker-user):

# Clones a git repository into the user's home folder
#!/bin/bash

set -eux

# Replace this with the URL of your git repository
export REPOSITORY_URL="https://github.com/aws-samples/sagemaker-studio-lifecycle-config-examples.git"

git -C /root clone $REPOSITORY_URL

The new modified script looks like the following:

#!/bin/bash
set -eux
STATUS=$(
python3 -c "import sagemaker_dataprep"
echo $?
)
if [ "$STATUS" -eq 0 ]; then
echo 'Instance is of Type Data Wrangler'
else
echo 'Instance is not of Type Data Wrangler'

# Replace this with the URL of your git repository
export REPOSITORY_URL="https://github.com/aws-samples/sagemaker-studio-lifecycle-config-examples.git"

git -C /root clone $REPOSITORY_URL

fi

You can save this script as git_command_test.sh.

Now you run a series of commands in your terminal or command prompt. You should configure the AWS Command Line Interface (AWS CLI) to interact with AWS. If you haven’t set up the AWS CLI, refer to Configuring the AWS CLI.

1. Convert your git_command_test.sh file into Base64 format. This requirement prevents errors due to the encoding of spacing and line breaks.

   LCC_GIT=openssl base64 -A -in /Users/abcde/Downloads/git_command_test.sh

2. Create a Studio lifecycle configuration. The following command creates a lifecycle configuration that runs on launch of an associated Kernel Gateway app:

   aws sagemaker create-studio-lifecycle-config —region us-east-2 —studio-lifecycle-config-name lcc-git —studio-lifecycle-config-content $LCC_GIT —studio-lifecycle-config-app-type KernelGateway

3. Use the following API call to create a new user profile with an associated lifecycle configuration:

   aws sagemaker create-user-profile --domain-id d-vqc14vvvvvvv 
   --user-profile-name test 
   --region us-east-2 
   --user-settings '{
   "KernelGatewayAppSettings": {
   "LifecycleConfigArns" : ["arn:aws:sagemaker:us-east-2:000000000000:studio-lifecycle-config/lcc-git"],
   "DefaultResourceSpec": {
   "InstanceType": "ml.m5.xlarge",
   "LifecycleConfigArn": "arn:aws:sagemaker:us-east-2:00000000000:studio-lifecycle-config/lcc-git"
   }
   }
   }'

   Alternatively, if you want to create a Studio domain to associate your lifecycle configuration at the domain level, or update the user profile or domain, you can follow the steps in Setting Default Lifecycle Configurations.

4. Now you can launch your Studio app from the SageMaker Control Panel.
5. In your Studio environment, on the File menu, choose New and Data Wrangler Flow.The new Data Wrangler flow should open without any issues.
   
6. To validate the Git clone, you can open a new Launcher in Studio.
   
7. Under Notebooks and compute resources, choose the Python 3 notebook and the Data Science SageMaker image to start your script as your default lifecycle configuration script.
   

You can see the Git cloned to /root in the following screenshot.

We have successfully applied the default Kernel lifecycle configuration at the user profile level and created a Data Wrangler flow. To configure at the Studio domain level, the only change is instead of creating a user profile, you pass the ARN of the lifecycle configuration in a create-domain call.

Apply lifecycle configuration at the application level

If you apply the default Kernel Gateway lifecycle configuration at the application level, you won’t have any issues because Data Wrangler skips the lifecycle configuration applied at the application level.

Conclusion

In this post, we showed how to configure your default lifecycle configuration properly for Studio when you use Data Wrangler for data preparation and visualization requirements.

To summarize, if you need to use the default lifecycle configuration for Studio to automate customization for your Studio environments and use Data Wrangler for data preparation, you can apply the default Kernel Gateway lifecycle configuration at the user profile or Studio domain level with the appropriate code block included in your lifecycle configuration so that the default lifecycle configuration checks it and skips the Data Wrangler Kernel Gateway app.

For more information, see the following resources:

• Amazon SageMaker Studio lifecycle configuration documentation
• Amazon SageMaker Studio
• Repository of example lifecycle configuration scripts
• Debugging Lifecycle Configurations

About the Authors

Rajakumar Sampathkumar is a Principal Technical Account Manager at AWS, providing customers guidance on business-technology alignment and supporting the reinvention of their cloud operation models and processes. He is passionate about cloud and machine learning. Raj is also a machine learning specialist and works with AWS customers to design, deploy, and manage their AWS workloads and architectures.

Vicky Zhang is a Software Development Engineer at Amazon SageMaker. She is passionate about problem solving. In her spare time, she enjoys watching detective movies and playing badminton.

Rahul Nabera is a Data Analytics Consultant in AWS Professional Services. His current work focuses on enabling customers build their data and machine learning workloads on AWS. In his spare time, he enjoys playing cricket and volleyball.

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