Monthly Archives: July 2023

Amazon Polly is a service that turns text into lifelike speech. It enables the development of a whole class of applications that can convert text into speech in multiple languages.

This service can be used by chatbots, audio books, and other text-to-speech applications in conjunction with other AWS AI or machine learning (ML) services. For example, Amazon Lex and Amazon Polly can be combined to create a chatbot that engages in a two-way conversation with a user and performs certain tasks based on the user’s commands. Amazon Transcribe, Amazon Translate, and Amazon Polly can be combined to transcribe speech to text in the source language, translate it to a different language, and speak it.

In this post, we present an interesting approach for highlighting text as it’s being spoken using Amazon Polly. This solution can be used in many text-to-speech applications to do the following:

• Add visual capabilities to audio in books, websites, and blogs
• Increase comprehension when customers are trying to understand the text rapidly as it’s being spoken

Our solution gives the client (the browser, in this example), the ability to know what text (word or sentence) is being spoken by Amazon Polly at any instant. This enables the client to dynamically highlight the text as it’s being spoken. Such a capability is useful for providing visual aid to speech for the use cases mentioned previously.

Our solution can be extended to perform additional tasks besides highlighting text. For example, the browser can show images, play music, or perform other animations on the front end as the text is being spoken. This capability is useful for creating dynamic audio books, educational content, and richer text-to-speech applications.

Solution overview

At its core, the solution uses Amazon Polly to convert a string of text into speech. The text can be input from the browser or through an API call to the endpoint exposed by our solution. The speech generated by Amazon Polly is stored as an audio file (MP3 format) in an Amazon Simple Storage Service (Amazon S3) bucket.

However, using the audio file alone, the browser can’t find what parts of the text are being spoken at any instant because we don’t have granular information on when each word is spoken.

Amazon Polly provides a way to obtain this using speech marks. Speech marks are stored in a text file that shows the time (measured in milliseconds from start of the audio) when each word or sentence is spoken.

Amazon Polly returns speech mark objects in a line-delimited JSON stream. A speech mark object contains the following fields:

• Time – The timestamp in milliseconds from the beginning of the corresponding audio stream
• Type – The type of speech mark (sentence, word, viseme, or SSML)
• Start – The offset in bytes (not characters) of the start of the object in the input text (not including viseme marks)
• End – The offset in bytes (not characters) of the object’s end in the input text (not including viseme marks)
• Value – This varies depending on the type of speech mark:
  • SSML – <mark> SSML tag
  • Viseme – The viseme name
  • Word or sentence – A substring of the input text as delimited by the start and end fields

For example, the sentence “Mary had a little lamb” can give you the following speech marks file if you use SpeechMarkTypes = [“word”, “sentence”] in the API call to obtain the speech marks:

{"time":0,"type":"sentence","start":0,"end":23,"value":"Mary had a little lamb."}
{"time":6,"type":"word","start":0,"end":4,"value":"Mary"}
{"time":373,"type":"word","start":5,"end":8,"value":"had"}
{"time":604,"type":"word","start":9,"end":10,"value":"a"}
{"time":643,"type":"word","start":11,"end":17,"value":"little"}
{"time":882,"type":"word","start":18, "end":22,"value":"lamb"}

The word “had” (at the end of line 3) begins 373 milliseconds after the audio stream begins, starts at byte 5, and ends at byte 8 of the input text.

Architecture overview

The architecture of our solution is presented in the following diagram.

Highlight Text as it’s spoken, using Amazon Polly

Our website for the solution is stored on Amazon S3 as static files (JavaScript, HTML), which are hosted in Amazon CloudFront (1) and served to the end-user’s browser (2).

When the user enters text in the browser through a simple HTML form, it’s processed by JavaScript in the browser. This calls an API (3) through Amazon API Gateway, to invoke an AWS Lambda function (4). The Lambda function calls Amazon Polly (5) to generate speech (audio) and speech marks (JSON) files. Two calls are made to Amazon Polly to fetch the audio and speech marks files. The calls are made using JavaScript async functions. The output of these calls is the audio and speech marks files, which are stored in Amazon S3 (6a). To avoid multiple users overwriting each others’ files in the S3 bucket, the files are stored in a folder with a timestamp. This minimizes the chances of two users overwriting each others’ files in Amazon S3. For a production release, we can employ more robust approaches to segregate users’ files based on user ID or timestamp and other unique characteristics.

The Lambda function creates pre-signed URLs for the speech and speech marks files and returns them to the browser in the form of an array (7, 8, 9).

When the browser sends the text file to the API endpoint (3), it gets back two pre-signed URLs for the audio file and the speech marks file in one synchronous invocation (9). This is indicated by the key symbol next to the arrow.

A JavaScript function in the browser fetches the speech marks file and the audio from their URL handles (10). It sets up the audio player to play the audio. (The HTML audio tag is used for this purpose).

When the user clicks the play button, it parses the speech marks retrieved in the earlier step to create a series of timed events using timeouts. The events invoke a callback function, which is another JavaScript function used to highlight the spoken text in the browser. Simultaneously, the JavaScript function streams the audio file from its URL handle.

The result is that the events are run at the appropriate times to highlight the text as it’s spoken while the audio is being played. The use of JavaScript timeouts provides us the synchronization of the audio with the highlighted text.

Prerequisites

To run this solution, you need an AWS account with an AWS Identity and Access Management (IAM) user who has permission to use Amazon CloudFront, Amazon API Gateway, Amazon Polly, Amazon S3, AWS Lambda, and AWS Step Functions.

Use Lambda to generate speech and speech marks

The following code invokes the Amazon Polly synthesize_speech function two times to fetch the audio and speech marks file. They’re run as asynchronous functions and coordinated to return the result at the same time using promises.

const p1 = new Promise(doSynthesizeSpeech marks);
const p2 = new Promise(doSynthesizeSpeech);
var result;

await Promise.all([p1, p2])
.then((values) => {
//return array of presigned urls 
     console.log('Values:', values);
     result = { "output" : values };
})
.catch((err) => {
     console.log("Error:" + err);
     result = err;
});

On the JavaScript side, the text highlighting is done by highlighter(start, finish, word) and the timed events are set by setTimers():

function highlighter(start, finish, word) {
     let textarea = document.getElementById("postText");
     //console.log(start + "," + finish + "," + word);
     textarea.focus();
     textarea.setSelectionRange(start, finish);
}

function setTimers() {
     let speech marksStr = sessionStorage.getItem("speech marks");
     //read through the speech marks file and set timers for every word
     console.log(speech marksStr);
     let speech marks = speech marksStr.split("n");
     for (let i = 0; i < speech marks.length; i++) {
          //console.log(i + ":" + speech marks[i]);
          if (speech marks[i].length == 0) {
               continue;
     }

     smjson = JSON.parse(speech marks[i]);
     t = smjson["time"];
     s = smjson["start"];
     f = smjson["end"]; 
     word = smjson["value"];
     setTimeout(highlighter, t, s, f, word);
     }
}

Alternative approaches

Instead of the previous approach, you can consider a few alternatives:

• Create both the speech marks and audio files inside a Step Functions state machine. The state machine can invoke the parallel branch condition to invoke two different Lambda functions: one to generate speech and another to generate speech marks. The code for this can be found in the using-step-functions subfolder in the Github repo.
• Invoke Amazon Polly asynchronously to generate the audio and speech marks. This approach can be used if the text content is large or the user doesn’t need a real-time response. For more details about creating long audio files, refer to Creating Long Audio Files.
• Have Amazon Polly create the presigned URL directly using the generate_presigned_url call on the Amazon Polly client in Boto3. If you go with this approach, Amazon Polly generates the audio and speech marks newly every time. In our current approach, we store these files in Amazon S3. Although these stored files aren’t accessible from the browser in our version of the code, you can modify the code to play previously generated audio files by fetching them from Amazon S3 (instead of regenerating the audio for the text again using Amazon Polly). We have more code examples for accessing Amazon Polly with Python in the AWS Code Library.

Create the solution

The entire solution is available from our Github repo. To create this solution in your account, follow the instructions in the README.md file. The solution includes an AWS CloudFormation template to provision your resources.

Cleanup

To clean up the resources created in this demo, perform the following steps:

1. Delete the S3 buckets created to store the CloudFormation template (Bucket A), the source code (Bucket B) and the website (pth-cf-text-highlighter-website-[Suffix]).
2. Delete the CloudFormation stack pth-cf.
3. Delete the S3 bucket containing the speech files (pth-speech-[Suffix]). This bucket was created by the CloudFormation template to store the audio and speech marks files generated by Amazon Polly.

Summary

In this post, we showed an example of a solution that can highlight text as it’s being spoken using Amazon Polly. It was developed using the Amazon Polly speech marks feature, which provides us markers for the place each word or sentence begins in an audio file.

The solution is available as a CloudFormation template. It can be deployed as is to any web application that performs text-to-speech conversion. This would be useful for adding visual capabilities to audio in books, avatars with lip-sync capabilities (using viseme speech marks), websites, and blogs, and for aiding people with hearing impairments.

It can be extended to perform additional tasks besides highlighting text. For example, the browser can show images, play music, and perform other animations on the front end while the text is being spoken. This capability can be useful for creating dynamic audio books, educational content, and richer text-to-speech applications.

We welcome you to try out this solution and learn more about the relevant AWS services from the following links. You can extend the functionality for your specific needs.

• Amazon API Gateway
• Amazon CloudFront
• AWS Lambda
• Amazon Polly
• Amazon S3

About the Author

Varad G Varadarajan is a Trusted Advisor and Field CTO for Digital Native Businesses (DNB) customers at AWS. He helps them architect and build innovative solutions at scale using AWS products and services. Varad’s areas of interest are IT strategy consulting, architecture, and product management. Outside of work, Varad enjoys creative writing, watching movies with family and friends, and traveling.

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Predictive maintenance is critical in automotive industries because it can avoid out-of-the-blue mechanical failures and reactive maintenance activities that disrupt operations. By predicting vehicle failures and scheduling maintenance and repairs, you’ll reduce downtime, improve safety, and boost productivity levels.

What if we could apply deep learning techniques to common areas that drive vehicle failures, unplanned downtime, and repair costs?

In this post, we show you how to train and deploy a model to predict vehicle fleet failure probability using Amazon SageMaker JumpStart. SageMaker Jumpstart is the machine learning (ML) hub of Amazon SageMaker, providing pre-trained, publicly available models for a wide range of problem types to help you get started with ML. The solution outlined in the post is available on GitHub.

SageMaker JumpStart solution templates

SageMaker JumpStart provides one-click, end-to-end solutions for many common ML use cases. Explore the following use cases for more information on available solution templates:

• Demand forecasting
• Credit rating prediction
• Fraud detection
• Computer vision
• Extract and analyze data from documents
• Predictive maintenance
• Churn prediction
• Personalized recommendations
• Reinforcement learning
• Healthcare and life sciences
• Financial pricing

The SageMaker JumpStart solution templates cover a variety of use cases, under each of which several different solution templates are offered (the solution in this post, Predictive Maintenance for Vehicle Fleets, is in the Solutions section). Choose the solution template that best fits your use case from the SageMaker JumpStart landing page. For more information on specific solutions under each use case and how to launch a SageMaker JumpStart solution, see Solution Templates.

Solution overview

The AWS predictive maintenance solution for automotive fleets applies deep learning techniques to common areas that drive vehicle failures, unplanned downtime, and repair costs. It serves as an initial building block for you to get to a proof of concept in a short period of time. This solution contains data preparation and visualization functionality within SageMaker and allows you to train and optimize the hyperparameters of deep learning models for your dataset. You can use your own data or try the solution with a synthetic dataset as part of this solution. This version processes vehicle sensor data over time. A subsequent version will process maintenance record data.

The following diagram demonstrates how you can use this solution with SageMaker components. As part of the solution, the following services are used:

• Amazon S3 – We use Amazon Simple Storage Service (Amazon S3) to store datasets
• SageMaker notebook – We use a notebook to preprocess and visualize the data, and to train the deep learning model
• SageMaker endpoint – We use the endpoint to deploy the trained model

The workflow includes the following steps:

1. An extract of historical data is created from the Fleet Management System containing vehicle data and sensor logs.
2. After the ML model is trained, the SageMaker model artifact is deployed.
3. The connected vehicle sends sensor logs to AWS IoT Core (alternatively, via an HTTP interface).
4. Sensor logs are persisted via Amazon Kinesis Data Firehose.
5. Sensor logs are sent to AWS Lambda for querying against the model to make predictions.
6. Lambda sends sensor logs to Sagemaker model inference for predictions.
7. Predictions are persisted in Amazon Aurora.
8. Aggregate results are displayed on an Amazon QuickSight dashboard.
9. Real-time notifications on the predicted probability of failure are sent to Amazon Simple Notification Service (Amazon SNS).
10. Amazon SNS sends notifications back to the connected vehicle.

The solution consists of six notebooks:

• 0_demo.ipynb – A quick preview of our solution
• 1_introduction.ipynb – Introduction and solution overview
• 2_data_preparation.ipynb – Prepare a sample dataset
• 3_data_visualization.ipynb – Visualize our sample dataset
• 4_model_training.ipynb – Train a model on our sample dataset to detect failures
• 5_results_analysis.ipynb – Analyze the results from the model we trained

Prerequisites

Amazon SageMaker Studio is the integrated development environment (IDE) within SageMaker that provides us with all the ML features that we need in a single pane of glass. Before we can run SageMaker JumpStart, we need to set up SageMaker Studio. You can skip this step if you already have your own version of SageMaker Studio running.

The first thing we need to do before we can use any AWS services is to make sure we have signed up for and created an AWS account. Then we create an administrative user and a group. For instructions on both steps, refer to Set Up Amazon SageMaker Prerequisites.

The next step is to create a SageMaker domain. A domain sets up all the storage and allows you to add users to access SageMaker. For more information, refer to Onboard to Amazon SageMaker Domain. This demo is created in the AWS Region us-east-1.

Finally, you launch SageMaker Studio. For this post, we recommend launching a user profile app. For instructions, refer to Launch Amazon SageMaker Studio.

To run this SageMaker JumpStart solution and have the infrastructure deployed to your AWS account, you need to create an active SageMaker Studio instance (see Onboard to Amazon SageMaker Studio). When your instance is ready, use the instructions in SageMaker JumpStart to launch the solution. The solution artifacts are included in this GitHub repository for reference.

Launch the SageMaker Jumpstart solution

To get started with the solution, complete the following steps:

1. On the SageMaker Studio console, choose JumpStart.
   
2. On the Solutions tab, choose Predictive Maintenance for Vehicle Fleets.
   
3. Choose Launch.
   
   It takes a few minutes to deploy the solution.
4. After the solution is deployed, choose Open Notebook.
   

If you’re prompted to select a kernel, choose PyTorch 1.8 Python 3.6 for all notebooks in this solution.

Solution preview

We first work on the 0_demo.ipynb notebook. In this notebook, you can get a quick preview of what the outcome will look like when you complete the full notebook for this solution.

Choose Run and Run All Cells to run all cells in SageMaker Studio (or Cell and Run All in a SageMaker notebook instance). You can run all the cells in each notebook one after the other. Ensure all the cells finish processing before moving to the next notebook.

This solution relies on a config file to run the provisioned AWS resources. We generate the file as follows:

import boto3
import os
import json

client = boto3.client('servicecatalog')
cwd = os.getcwd().split('/')
i= cwd.index('S3Downloads')
pp_name = cwd[i + 1]
pp = client.describe_provisioned_product(Name=pp_name)
record_id = pp['ProvisionedProductDetail']['LastSuccessfulProvisioningRecordId']
record = client.describe_record(Id=record_id)

keys = [ x['OutputKey'] for x in record['RecordOutputs'] if 'OutputKey' and 'OutputValue' in x]
values = [ x['OutputValue'] for x in record['RecordOutputs'] if 'OutputKey' and 'OutputValue' in x]
stack_output = dict(zip(keys, values))

with open(f'/root/S3Downloads/{pp_name}/stack_outputs.json', 'w') as f:
json.dump(stack_output, f)

We have some sample time series input data consisting of a vehicle’s battery voltage and battery current over time. Next, we load and visualize the sample data. As shown in the following screenshots, the voltage and current values are on the Y axis and the readings (19 readings recorded) are on the X axis.

We have previously trained a model on this voltage and current data that predicts the probability of vehicle failure and have deployed the model as an endpoint in SageMaker. We will call this endpoint with some sample data to determine the probability of failure in the next time period.

Given the sample input data, the predicted probability of failure is 45.73%.

To move to the next stage, choose Click here to continue.

Introduction and solution overview

The 1_introduction.ipynb notebook provides an overview of the solution and stages, and a look into the configuration file that has content definition, data sampling period, train and test sample count, parameters, location, and column names for generated content.

After you review this notebook, you can move to the next stage.

Prepare a sample dataset

We prepare a sample dataset in the 2_data_preparation.ipynb notebook.

We first generate the configuration file for this solution:

import boto3
import os
import json

client = boto3.client('servicecatalog')
cwd = os.getcwd().split('/')
i= cwd.index('S3Downloads')
pp_name = cwd[i + 1]
pp = client.describe_provisioned_product(Name=pp_name)
record_id = pp['ProvisionedProductDetail']['LastSuccessfulProvisioningRecordId']
record = client.describe_record(Id=record_id)

keys = [ x['OutputKey'] for x in record['RecordOutputs'] if 'OutputKey' and 'OutputValue' in x]
values = [ x['OutputValue'] for x in record['RecordOutputs'] if 'OutputKey' and 'OutputValue' in x]
stack_output = dict(zip(keys, values))

with open(f'/root/S3Downloads/{pp_name}/stack_outputs.json', 'w') as f:
json.dump(stack_output, f)
import os

from source.config import Config
from source.preprocessing import pivot_data, sample_dataset
from source.dataset import DatasetGenerator
config = Config(filename="config/config.yaml", fetch_sensor_headers=False)
config

The config properties are as follows:

fleet_info_fn=data/example_fleet_info.csv
fleet_sensor_logs_fn=data/example_fleet_sensor_logs.csv
vehicle_id_column=vehicle_id
timestamp_column=timestamp
target_column=target
period_ms=30000
dataset_size=25000
window_length=20
chunksize=10000
processing_chunksize=2500
fleet_dataset_fn=data/processed/fleet_dataset.csv
train_dataset_fn=data/processed/train_dataset.csv
test_dataset_fn=data/processed/test_dataset.csv
period_column=period_ms

You can define your own dataset or use our scripts to generate a sample dataset:

if should_generate_data:
    fleet_statistics_fn = "data/generation/fleet_statistics.csv"
    generator = DatasetGenerator(fleet_statistics_fn=fleet_statistics_fn,
                                 fleet_info_fn=config.fleet_info_fn, 
                                 fleet_sensor_logs_fn=config.fleet_sensor_logs_fn, 
                                 period_ms=config.period_ms, 
                                 )
    generator.generate_dataset()

assert os.path.exists(config.fleet_info_fn), "Please copy your data to {}".format(config.fleet_info_fn)
assert os.path.exists(config.fleet_sensor_logs_fn), "Please copy your data to {}".format(config.fleet_sensor_logs_fn)

You can merge the sensor data and fleet vehicle data together:

pivot_data(config)
sample_dataset(config)

We can now move to data visualization.

Visualize our sample dataset

We visualize our sample dataset in 3_data_vizualization.ipynb. This solution relies on a config file to run the provisioned AWS resources. Let’s generate the file similar to the previous notebook.

The following screenshot shows our dataset.

Next, let’s build the dataset:

train_ds = PMDataset_torch(
    config.train_dataset_fn,
    sensor_headers=config.sensor_headers,
    target_column=config.target_column,
    standardize=True)

properties = train_ds.vehicle_properties_headers.copy()
properties.remove('vehicle_id')
properties.remove('timestamp')
properties.remove('period_ms')

Now that the dataset is ready, let’s visualize the data statistics. The following screenshot shows the data distribution based on vehicle make, engine type, vehicle class, and model.

Comparing the log data, let’s look at an example of the mean voltage across different years for Make E and C (random).

The mean of voltage and current is on the Y axis and the number of readings is on the X axis.

• Possible values for log_target: [‘make’, ‘model’, ‘year’, ‘vehicle_class’, ‘engine_type’]
  • Randomly assigned value for log_target: make
• Possible values for log_target_value1: [‘Make A’, ‘Make B’, ‘Make E’, ‘Make C’, ‘Make D’]
  • Randomly assigned value for log_target_value1: Make B
• Possible values for log_target_value2: [‘Make A’, ‘Make B’, ‘Make E’, ‘Make C’, ‘Make D’]
  • Randomly assigned value for log_target_value2: Make D

Based on the above, we assume log_target: make, log_target_value1: Make B and log_target_value2: Make D

The following graphs break down the mean of the log data.

The following graphs visualize an example of different sensor log values against voltage and current.

Train a model on our sample dataset to detect failures

In the 4_model_training.ipynb notebook, we train a model on our sample dataset to detect failures.

Let’s generate the configuration file similar to the previous notebook, and then proceed with training configuration:

sage_session = sagemaker.session.Session()
s3_bucket = sagemaker_configs["S3Bucket"]  
s3_output_path = 's3://{}/'.format(s3_bucket)
print("S3 bucket path: {}".format(s3_output_path))

# run in local_mode on this machine, or as a SageMaker TrainingJob
local_mode = False

if local_mode:
    instance_type = 'local'
else:
    instance_type = sagemaker_configs["SageMakerTrainingInstanceType"]
    
role = sagemaker.get_execution_role()
print("Using IAM role arn: {}".format(role))
# only run from SageMaker notebook instance
if local_mode:
    !/bin/bash ./setup.sh
cpu_or_gpu = 'gpu' if instance_type.startswith('ml.p') else 'cpu'

%%time

estimator = PyTorch(entry_point="train.py",
                    source_dir='source',                    
                    role=role,
                    dependencies=["source/dl_utils"],
                    instance_type=instance_type,
                    instance_count=1,
                    output_path=s3_output_path,
                    framework_version="1.5.0",
                    py_version='py3',
                    base_job_name=job_name_prefix,
                    metric_definitions=metric_definitions,
                    hyperparameters= {
                        'epoch': 100,  # tune it according to your need
                        'target_column': config.target_column,
                        'sensor_headers': json.dumps(config.sensor_headers),
                        'train_input_filename': os.path.basename(config.train_dataset_fn),
                        'test_input_filename': os.path.basename(config.test_dataset_fn),
                        }
                     )

if local_mode:
    estimator.fit({'train': training_data, 'test': testing_data})
%%time

tuner = HyperparameterTuner(estimator,
                            objective_metric_name='test_auc',
                            objective_type='Maximize',
                            hyperparameter_ranges=hyperparameter_ranges,
                            metric_definitions=metric_definitions,
                            max_jobs=max_jobs,
                            max_parallel_jobs=max_parallel_jobs,
                            base_tuning_job_name=job_name_prefix)
tuner.fit({'train': training_data, 'test': testing_data})

Analyze the results from the model we trained

In the 5_results_analysis.ipynb notebook, we get data from our hyperparameter tuning job, visualize metrics of all the jobs to identify the best job, and build an endpoint for the best training job.

Let’s generate the configuration file similar to the previous notebook and visualize the metrics of all the jobs. The following plot visualizes test accuracy vs. epoch.

The following screenshot shows the hyperparameter tuning jobs we ran.

You can now visualize data from the best training job (out of the four training jobs) based on the test accuracy (red).

As we can see in the following screenshots, the test loss declines and AUC and accuracy increase with epochs.

Based on the visualizations, we can now build an endpoint for the best training job:

%%time

role = sagemaker.get_execution_role()

model = PyTorchModel(model_data=model_artifact,
                     role=role,
                     entry_point="inference.py",
                     source_dir="source/dl_utils",
                     framework_version='1.5.0',
                     py_version = 'py3',
                     name=sagemaker_configs["SageMakerModelName"],
                     code_location="s3://{}/endpoint".format(s3_bucket)
                    )

endpoint_instance_type = sagemaker_configs["SageMakerInferenceInstanceType"]

predictor = model.deploy(initial_instance_count=1, instance_type=endpoint_instance_type, endpoint_name=sagemaker_configs["SageMakerEndpointName"])

def custom_np_serializer(data):
    return json.dumps(data.tolist())
    
def custom_np_deserializer(np_bytes, content_type='application/x-npy'):
    out = np.array(json.loads(np_bytes.read()))
    return out

predictor.serializer = custom_np_serializer
predictor.deserializer = custom_np_deserializer

After we build the endpoint, we can test the predictor by passing it sample sensor logs:

import botocore

config = botocore.config.Config(read_timeout=200)
runtime = boto3.client('runtime.sagemaker', config=config)

data = np.ones(shape=(1, 20, 2)).tolist()
payload = json.dumps(data)

response = runtime.invoke_endpoint(EndpointName=sagemaker_configs["SageMakerEndpointName"],
ContentType='application/json',
Body=payload)
out = json.loads(response['Body'].read().decode())[0]

print("Given the sample input data, the predicted probability of failure is {:0.2f}%".format(100*(1.0-out[0])))

Given the sample input data, the predicted probability of failure is 34.60%.

Clean up

When you’ve finished with this solution, make sure that you delete all unwanted AWS resources. On the Predictive Maintenance for Vehicle Fleets page, under Delete solution, choose Delete all resources to delete all the resources associated with the solution.

You need to manually delete any extra resources that you may have created in this notebook. Some examples include the extra S3 buckets (to the solution’s default bucket) and the extra SageMaker endpoints (using a custom name).

Customize the solution

Our solution is simple to customize. To modify the input data visualizations, refer to sagemaker/3_data_visualization.ipynb. To customize the machine learning, refer to sagemaker/source/train.py and sagemaker/source/dl_utils/network.py. To customize the dataset processing, refer to sagemaker/1_introduction.ipynb on how to define the config file.

Additionally, you can change the configuration in the config file. The default configuration is as follows:

fleet_info_fn=data/example_fleet_info.csv
fleet_sensor_logs_fn=data/example_fleet_sensor_logs.csv
vehicle_id_column=vehicle_id
timestamp_column=timestamp
target_column=target
period_ms=30000
dataset_size=10000
window_length=20
chunksize=10000
processing_chunksize=1000
fleet_dataset_fn=data/processed/fleet_dataset.csv
train_dataset_fn=data/processed/train_dataset.csv
test_dataset_fn=data/processed/test_dataset.csv
period_column=period_ms

The config file has the following parameters:

• fleet_info_fn, fleet_sensor_logs_fn, fleet_dataset_fn, train_dataset_fn, and test_dataset_fn define the location of dataset files
• vehicle_id_column, timestamp_column, target_column, and period_column define the headers for columns
• dataset_size, chunksize, processing_chunksize, period_ms, and window_length define the properties of the dataset

Conclusion

In this post, we showed you how to train and deploy a model to predict vehicle fleet failure probability using SageMaker JumpStart. The solution is based on ML and deep learning models and allows a wide variety of input data including any time-varying sensor data. Because every vehicle has different telemetry on it, you can fine-tune the provided model to the frequency and type of data that you have.

To learn more about what you can do with SageMaker JumpStart, refer to the following:

• Visual inspection automation using Amazon SageMaker JumpStart
• Run automatic model tuning with Amazon SageMaker JumpStart
• Get started with generative AI on AWS using Amazon SageMaker JumpStart

Resources

• Amazon SageMaker Developer Guide
• SageMaker JumpStart Developer Guide
• Perform Automatic Model Tuning with SageMaker
• SageMaker JumpStart predictive maintenance solution

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.

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