A.I. Kung Fu

Friday, October 30, 2020

Deploying and using the Document Understanding Solution

Based on our day to day experience, the information we consume is entirely digital. We read the news on our mobile devices far more than we do from printed copy newspapers. Tickets for sporting events, music concerts, and airline travel are stored in apps on our phones. One could go weeks or longer without needing to have any paper currency in his or her wallet, as digital payments are ubiquitous. However, many companies across different industries still primarily operate on manual, paper-based processes. For example, healthcare payors, construction companies, and law firms deal with billions of documents and forms, making the process of finding information difficult and time-consuming. When documents are found, extracting information through manual data entry can be slow, expensive, and error prone, resulting in increases in compliance risks. Furthermore, domain experts need to identify and categorize domain-specific phrases and keywords (or entities), or use traditional Optical Character Recognition (OCR) and keyword detection software that requires manual customization. These approaches can create scrambled output and unusable results. AWS AI services such as Amazon Kendra, Amazon Textract, Amazon Comprehend, and Amazon Comprehend Medical help solve these challenges by automating data extraction and comprehension using machine learning (ML).

Overview of the Document Understanding Solution

The Document Understanding Solution (DUS) allows you to use the power of AWS AI for enterprise search, document digitization, discovery, and extraction and redaction of select information. Part of the Intelligent Document Processing services offered from AWS, this solution uses AWS artificial intelligence (AI) services to solve business problems.

Search and discovery

These challenges exist in almost every business vertical. Imagine a manufacturer that has to maintain archives of thousands – if not millions – of product and tool specifications. Without document digitization of archives, there could be massive underutilization of their highly valuable tool data and information retrieval could be complex and costly. In another example, a company in the financial industry could have 1000s of financial reports in paper format. Without a simple way to extract and digitize this data, it could take an extensive manual effort to keypunch it.

To help with these situations, DUS leverages multiple ML services, including Amazon Textract. Amazon Textract is a fully managed machine learning service that automatically extracts text and data from scanned documents that goes beyond simple optical character recognition (OCR) to identify, understand, and extract data from forms and tables. Amazon Textract will move the data from the documents to a format that can be readily searched. Next, Amazon Kendra and Amazon Elasticsearch Service (Amazon ES) are available to provide the end user search experience in DUS. Amazon Kendra is an intelligent search service powered by machine learning. Amazon Kendra uses ML to obtain better results for natural language questions, and will return an exact answer from within a document, whether that is a text snippet, FAQ, or a PDF document. In addition to Amazon Kendra, the DUS provides a rich search experience to the user through the use of Amazon Elasticsearch Service. Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and run Elasticsearch cost effectively at scale.

Control and compliance

In addition to search, the ability to analyze documents at scale is essential. Amazon Textract extracts text from documents, which can then be input into Amazon Comprehend or Amazon Comprehend Medical. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. It can identify key phrases and entities, such as places, people, and brands. Amazon Comprehend Medical is similar to Comprehend. It is a natural language processing service that makes it easy to use machine learning to extract relevant medical information from unstructured text. It can identify medical entities, such as medical conditions and medications.

Identifying these key pieces of information allows for compliance controls through redaction. For example, an insurer could use this solution to feed a workflow that automatically redacts personally identifiable information (PII) or protected health information (PHI) for their review before archiving claim forms by automatically recognizing the important key-value pairs and entities that require protection.

Other industries can also use this solution for complying with regulatory standards, such as GDPR and HIPAA. For example, this solution could be used by a law firm to redact PII, organization names or brand names. Another example includes a security agency needing to redact all vital information such as names, locations and/or dates from a case file for data security or privacy concerns.

Workflow automation

The DUS solution delivers results at scale in production workflows. Organizations can more rapidly process documents such as insurance claims and forms, and seamlessly extract tables from PDFs into CSVs to conduct additional analysis. With detection and categorization of medical entities and ICD-10-CM ontologies, medical institutes can recognize exponential savings in workforce, time, and other resources that are spent identifying and classifying patient information. All the data is stored by the solution in easily accessible formats, such as CSV and JSON files, which can be fed into downstream pipelines. Additionally, the bulk processing feature in DUS allows you to import a large number of documents directly for processing and analysis.

The following diagram illustrates the DUS architecture.

Deploying DUS

For instructions on setting up DUS, see Document Understanding Solution on AWS Solutions.

Deploying DUS sets up a web application that you can use for document understanding. The deployment includes setting up infrastructure in your AWS account and pre-loading sample documents.

Using DUS

Once you have successfully completed deploying the DUS demo, you are then provided with instructions on how to login into the application. After logging in, you are directed to the homepage, as seen below. You have three options which cover the common use-cases in document understanding solution: Discovery, Compliance, and Workflow Automation.

When you select the Discovery track you will be directed to the preloaded documents page or the Document List page. You may select one of the preloaded sample documents or upload your own document. From here, you can search for a specific document by using a phrase or keyword.

If you decide to upload your own document, choose upload your own documents above the available documents. You will then be directed to a new page to upload your own documents. This page also has sample documents from different industry verticals for you to experiment with.

Back on the Document List page, you will find some PDF and image files. Text in these documents are not actually tagged or available to use by default. However, since these documents have been processed by the solution, you will now be able to search for information within these documents. If you decide to search for a specific phrase or keyword in the search bar, then the solution will analyze the text it has extracted from the documents and provide you with search results. The search results can be displayed in three different ways; a comparative view of Amazon ES (traditional search) and Amazon Kendra (semantic search), just Amazon ES or just Amazon Kendra .

For Amazon Kendra results, you also have the option to provide feedback by either up-voting or down-voting an Amazon Kendra suggested answer.

Amazon Kendra also supports filtering based on user context. Under the Amazon Kendra results view, you can filter results based on the users for the preloaded documents. Click the Filter button to the right of the Amazon Kendra Results title. You can then select a persona and one of the suggested questions to display filtered results. Amazon Kendra will then rank results based on the selected persona. You can toggle between the various personas to compare how the results differ. For demonstration purposes, the Document Understanding Solution comes with preloaded documents and personas from the medical industry. You will be able to notice that based on the question and persona selected, results are ranked differently creating a more targeted search experience for the user.

From the Document List search results view, you can select a document that you want to further explore. This will direct you to the Document Details page. See the following image.

The following image shows the tool bar above the search bar, where you can choose to see different types of information from the document.

The tabs have the following functions:

Preview – Under this tab, you are able to view the original document as well as download a searchable PDF version of the document. This helps users to convert their documents – be it images or PDFs into easily searchable PDF files.
Raw Text – Under this tab, you can access all the text identified in the file.
Key-Value Pairs – Under this tab, key-value pairs from the document are highlighted. In this process, all forms in the document are identified and stored in a key-value pair format. If desired, you can download a CSV file of the key-value pairs. This is especially useful for organizations that have structured data and would want to automate their data extraction and storage workflows. For example, organizations that have a lot of forms like job applications or medical patient forms.
Tables – Under this tab, you can view all the tables identified in the document. Like the key-value pairs, you can download the tables in the CSV format. Companies dealing with balance sheets or with invoices would find this feature extremely useful since it allows users to easily convert tables, images and PDFs into CSV files which can then be used for further analysis.
Entities and Medical Entities – Under these tabs, you can find the general and medical entities in the document respectively. These entities include persons, locations, dates, PHI and medical information which helps organization to easily identify and extract critical medical data in a document.

For exploring redaction controls, choose the Compliance option on the toolbar. Here you can choose to redact information like key-value pairs, entities, medical entities or even keyword matches by switching to the respective tabs on the tool bar and choosing Redact. One example of how this feature may be useful is to consider a clinic that wants to redact PHI information before they decide to share medical records. Another example is an organization that wants to redact specific information identified as keylue pairs in forms present in their documents. As seen in the following image, you can redact information, download the redacted document and even clear redactions after use.

In terms of Workflow Automation, the Document Understanding Solution also provides some input and output capabilities via the AWS Console which makes it easier to integrate DUS into an existing pipeline. DUS supports a bulk document processing mode, in which you can simply input documents into an Amazon Simple Storage Service (Amazon S3) bucket which will be asynchronously analyzed and made available in the application. More information on bulk processing is available on the AWS Solutions Implementation Guide. Results from the different AWS AI services are all stored within Amazon S3 buckets and the corresponding metadata is available in Amazon DynamoDB tables. This helps users of the solution to build downstream pipelines from these datastores that hold the document analysis data.

Summary:

This post reviewed how you can integrate Amazon Textract, Amazon Comprehend, Amazon Comprehend Medical, and Amazon Kendra to conduct enterprise search, document digitization, document discovery, and extraction and redaction of select information.

To access the DUS source code, see Document Understanding Solution on GitHub. This solution has been made open source so that you can extend and incorporate the solution into your AWS workflows.

About the Authors

Simran Baxendale is a Program Manager in the Amazon Machine Learning Solutions Lab. She helps define, coordinate and execute program strategy for the demos applications team.

Curtis Bray is a manager in the Amazon Machine Learning Solutions Lab. He leads the demos applications team that focuses on building use case based demos that show customers how to unlock the power of AWS AI/ML services to solve real world business problems.

Alex Chirayath is an SDE in the Amazon Machine Learning Solutions Lab. He helps customers adopt AWS AI services by building solutions to address common business problems.

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Deploying and using the Document Understanding Solution

Overview of the Document Understanding Solution

Search and discovery

Control and compliance

Workflow automation

The following diagram illustrates the DUS architecture.

Deploying DUS

For instructions on setting up DUS, see Document Understanding Solution on AWS Solutions.

Deploying DUS sets up a web application that you can use for document understanding. The deployment includes setting up infrastructure in your AWS account and pre-loading sample documents.

Using DUS

For Amazon Kendra results, you also have the option to provide feedback by either up-voting or down-voting an Amazon Kendra suggested answer.

From the Document List search results view, you can select a document that you want to further explore. This will direct you to the Document Details page. See the following image.

The following image shows the tool bar above the search bar, where you can choose to see different types of information from the document.

The tabs have the following functions:

Preview – Under this tab, you are able to view the original document as well as download a searchable PDF version of the document. This helps users to convert their documents – be it images or PDFs into easily searchable PDF files.
Raw Text – Under this tab, you can access all the text identified in the file.
Key-Value Pairs – Under this tab, key-value pairs from the document are highlighted. In this process, all forms in the document are identified and stored in a key-value pair format. If desired, you can download a CSV file of the key-value pairs. This is especially useful for organizations that have structured data and would want to automate their data extraction and storage workflows. For example, organizations that have a lot of forms like job applications or medical patient forms.
Tables – Under this tab, you can view all the tables identified in the document. Like the key-value pairs, you can download the tables in the CSV format. Companies dealing with balance sheets or with invoices would find this feature extremely useful since it allows users to easily convert tables, images and PDFs into CSV files which can then be used for further analysis.
Entities and Medical Entities – Under these tabs, you can find the general and medical entities in the document respectively. These entities include persons, locations, dates, PHI and medical information which helps organization to easily identify and extract critical medical data in a document.

Summary:

To access the DUS source code, see Document Understanding Solution on GitHub. This solution has been made open source so that you can extend and incorporate the solution into your AWS workflows.

About the Authors

Simran Baxendale is a Program Manager in the Amazon Machine Learning Solutions Lab. She helps define, coordinate and execute program strategy for the demos applications team.

Alex Chirayath is an SDE in the Amazon Machine Learning Solutions Lab. He helps customers adopt AWS AI services by building solutions to address common business problems.

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Training and serving H2O models using Amazon SageMaker

Model training and serving steps are two essential pieces of a successful end-to-end machine learning (ML) pipeline. These two steps often require different software and hardware setups to provide the best mix for a production environment. Model training is optimized for a low-cost, feasible total run duration, scientific flexibility, and model interpretability objectives, whereas model serving is optimized for low cost, high throughput, and low latency objectives.

Therefore, a wide-spread approach is to train a model with a popular data science language like Python or R, and create model artifact formats such as Model Object, Optimized (MOJO), Predictive Model Markup Language (PMML) or Open Neural Network Exchange (ONNX) and serve the model on a microservice (e.g., Spring Boot application) based on Open Java Development Kit (OpenJDK).

This post demonstrates how to implement this approach end-to-end using Amazon SageMaker for the popular open-source ML framework H2O. Amazon SageMaker is a fully managed service that provides every developer and data scientist the ability to build, train, and deploy ML models quickly. Amazon SageMaker is a versatile ML service, which allows you to use ML frameworks and programming languages of your choice. H2O was founded by H2O.ai, an AWS Partner Network (APN) Advanced Partner. You can choose from a wide range of options to train and deploy H2O models on the AWS Cloud, and H2O provides some design pattern examples to productionize H2O ML pipelines.

The H2O framework supports three type of model artifacts, as summarized in the following table.

Dimension	Binary Models	Plain Old Java Object (POJO)	Model Object, Optimized (MOJO)
Definition	The H2O binary model is intended for non-production ML experimentation with the features supported by a specific H2O version.	A POJO is an ordinary Java object, not bounded by any special restriction. It’s a way to export a model built in H2O and implement it in a Java application.	A MOJO is also a Java object, but the model tree is out of this object, because it has a generic tree-walker code to navigate the model. This allows model artifacts to be much smaller.
Use case	Intended for interactive ML experimentation.	Suitable for production usage.	Suitable for production usage
Deployment Restrictions	The model hosting image should run an H2O cluster and the same h2o version as the binary model.	1 GB maximum model artifact file size restriction for H2O.	No size restriction for H2O.
Inference Performance	High latency (up to a few seconds)—not recommended for production.	Only slightly faster than MOJOs for binomial and regression models. Latency is typically in single-digit milliseconds.	Significant inference efficiency gains over POJOs for multi-nominal and large models. Latency is typically in single-digit milliseconds.

During my trials, I explored some of the design patterns that Amazon SageMaker manages end to end, summarized in the following table.

ID	Design Pattern	Advantages	Disadvantages
A	Train and deploy the model with the Amazon SageMaker Marketplace algorithm offered by H2O.ai	No effort is required to create any custom container and Amazon SageMaker algorithm resource.	An older version of the h2o Python library is available. All other disadvantages in option B also apply to this option.
B	Train using a custom container with h2o Python library. Export the model artifact as H2O binary model format. Serve the model using a custom container running a Flask application and running inference by h2o Python library.	It’s possible to use any version of the h2o Python library.	H2O binary model inference latency is significantly higher than MOJO artifacts. It’s prone to failures due to h2o Python library version incompatibility.
C	Train using a custom container with the h2o Python library. Export the model in MOJO format. Serve the model using a custom container running a Flask application and running inference by pyH2oMojo.	Because MOJO model format is supported, the model inference latency is lower than option B and it’s possible to use any version of the h2o Python library.	Using pyH2oMojo has a higher latency and it’s prone to failures due to weak support for continuously evolving H2O versions.
D	Train using a custom container with the h2o Python library. Export the model in MOJO format. Serve the model using a custom container based on Amazon Corretto running a Spring Boot application and h2o-genmodel Java library.	It’s possible to use any version of h2o Python library and h2o-genmodel libraries. It offers the lowest model inference latency.	The majority of data scientists prefer using only scripting languages.

It’s possible to add a few more options to the preceding list, especially if you want to run distributed training with Sparkling Water. After testing all these alternatives, I have concluded that design pattern D is the most suitable option for a wide range of use cases to productionize H2O. Design pattern D is built by a custom model training container with the h2o Python library and a custom model inference container with Spring Boot application and h2o-genmodel Java library. This post shows how to build an ML workflow based on this design pattern in the subsequent sections.

Problem and dataset

You can use the Titanic Passenger Survival dataset, which is publicly available thanks to Kaggle and encyclopedia-titanica, to build a predictive model that answers what kind of people are more likely to survive in a catastrophic shipwreck. It uses 11 independent variables such as age, gender, and passenger class to predict the binary classification target variable Survived. For this post, we split the original training dataset 80%/20% to create train.csv and validation.csv input files. The datasets are located under the /examples directory of the parent repository. This dataset requires features preprocessing operations like data imputation of null values for the Age feature and string indexing for Sex and Embarked features to train a model using the Gradient Boosting Machines (GBM) algorithm using the H2O framework.

Overview of solution

The solution in this post offers an ML training and deployment process orchestrated by AWS Step Functions and implemented with Amazon SageMaker. The following diagram illustrates the workflow.

This workflow is developed using a JSON-based language called Amazon State Language (ASL). The Step Functions API provides service integrations to Amazon SageMaker, child workflows, and other services.

Two Amazon Elastic Container Registry (Amazon ECR) images contain the code mentioned in design pattern D:

h2o-gbm-trainer – H2O model training Docker image running a Python application
h2o-gbm-predictor – H2O model inference Docker image running a Spring Boot application

The creation of a manifest.json file in an Amazon Simple Storage Service (Amazon S3) bucket initiates an event notification, which starts the pipeline. This file can be generated by a prior data preparation job, which creates the training and validation datasets during a periodical production run. Uploading this file triggers an AWS Lambda function, which collects the ML workflow run duration configurations from the manifest.json file and AWS Systems Manager Parameter Store and starts the ML workflow.

Prerequisites

Make sure that you complete all the prerequisites before starting deployment. Deploying and running this workflow involves two types of dependencies:

ML workflow infrastructure deployment:
- S3 bucket (<s3bucket>)
- ml-parameters.json file
- hyperparameters.json file
Dependencies for ML workflow execution:
- Training and inference images created in Amazon ECR
- Amazon SageMaker algorithm resource
- Training and validation datasets
- manifest.json file

Deploying the ML workflow infrastructure

The infrastructure required for this post is created with an AWS CloudFormation template compliant to AWS Serverless Application Model (AWS SAM), which simplifies how to define functions, state machines, and APIs for serverless applications. I calculated the cost for a test run is less than $1 in the eu-central-1 Region. For installation instructions, see Installation.

The deployment takes approximately 2 minutes. When it’s complete, the status switches to CREATE_COMPLETE for all stacks.

The nested stacks create three serverless applications:

ml-parameter-provider manages parameters required by ML workflows
sagemaker-model-tuner manages model tuning and training process
sagemaker-endpoint-deployer manages the auto-scaling model endpoint creation and update process.

Creating a model training Docker image

Amazon SageMaker launches this Docker image on Amazon SageMaker training instances in the runtime. It’s a slightly modified version of the open-sourced Docker image repository by our partner H2O.AI, which extends the Amazon Linux 2 Docker image. Only the training code and its required dependencies are preserved; the H2O version is upgraded and a functionality to export MOJO model artifacts is added.

Navigate to h2o-gbm-trainer repository in your command line. Optionally, you can test it in your local PC. Build and deploy the model training Docker image to Amazon ECR using the installation command.

Creating a model inference Docker image

Amazon SageMaker launches this Docker image on Amazon SageMaker model endpoint instances in the runtime. The Amazon Corretto Docker Image (amazoncorretto:8) is extended to provide dependencies with Amazon Linux 2 Docker image and Java settings required to launch a Spring Boot application.

Depending on an open-source distribution of OpenJDK has several drawbacks, such as backward incompatibility between minor releases, delays in bug fixing, security vulnerabilities like backports, and suboptimal performance for a production service. Therefore, I used Amazon Corretto, which is a no-cost, multiplatform, secure, production-ready downstream distribution of the OpenJDK. In addition, Corretto offers performance improvements (AWS Online Tech Talk) with respect to OpenJDK (openjdk:8-alpine), which are observable during the Spring Boot application launch and model inference latency. The Spring Boot framework is preferred to build the model hosting application for the following reasons:

It’s easy to build a standalone production-grade microservice
It requires minimal Spring configuration and easy deployment
It’s easy to build RESTful web services
It scales the system resource utilization according to the intensity of the model invocations

The following image is the class diagram of the Spring Boot application created for the H2O GBM model predictor.

SagemakerController class is an entry point of this Spring Boot Java application, launched by SagemakerLauncher class in the model inference Docker image. SagemakerController class initializes the service in init() method by loading the H2O MOJO model artifact from Amazon S3 with H2O settings to impute the missing model scoring input features and loading a predictor object.

SagemakerController class also provides the /ping and /invocations REST API interfaces required by Amazon SageMaker, which are called by asynchronous and concurrent HTTPS requests to Amazon SageMaker model endpoint instances in the runtime. Amazon SageMaker reserves the /ping path for health checks during the model endpoint deployment. The /invocations path is mapped to the invoke() method, which forwards the incoming model invocation requests to the predict() method of the predictor object asynchronously. This predict() method uses Amazon SageMaker instance resources dedicated to the model inference Docker image efficiently thanks to its non-blocking asynchronous and concurrent calls.

Navigate to the h2o-gbm-predictor repository in your command line. Optionally, you can test it in your local PC. Build and deploy the model inference Docker image to Amazon ECR using the installation command.

Creating a custom Amazon SageMaker algorithm resource

After publishing the model training and inference Docker images on Amazon ECR, it’s time to create an Amazon SageMaker algorithm resource called h2o-gbm-algorithm. As displayed in the following diagram, an Amazon SageMaker algorithm resource contains training and inference Docker image URIs, Amazon SageMaker instance types, input channels, supported hyperparameters, and algorithm evaluation metrics.

Navigate to the h2o-gbm-algorithm-resource repository in your command line. Then run the installation command to create your algorithm resource.

After a few seconds, an algorithm resource is created.

Because all the required infrastructure components are now deployed, it’s time to run the ML pipeline to train and deploy H2O models.

Running the ML workflow

To start running your workflow, complete the following steps:

Upload the train.csv and validation.csv files to their dedicated directories in the <s3bucket> bucket (replace <s3bucket> with the S3 bucket name in the manifest.json file):

aws s3 cp examples/train.csv s3://<s3bucket>/titanic/training/
aws s3 cp examples/validation.csv s3://<s3bucket>/titanic/validation/

Upload the file under the s3://<s3bucket>/manifests directory located in the same S3 bucket specified during the ML workflow deployment:

aws s3 cp examples/manifest.json s3://<s3bucket>/manifests

As soon as the manifest.json file is uploaded to Amazon S3, Step Functions puts the ML workflow in a Running state.

Training the H2O model using Amazon SageMaker

To train your H2O model, complete the following steps:

On the Step Functions console, navigate to ModelTuningWithEndpointDeploymentStateMachine to find it in Running state and observe the Model Tuning Job step.

On the Amazon SageMaker console, under Training, choose Hyperparameter tuning jobs.
Drill down to the tuning job in progress.

After 4 minutes, all training jobs and the model tuning job change to Completed status.

The following screenshot shows the performance and configuration details of the best training job.

Navigate to the Amazon SageMaker model link to display the model definition in detail.

The following screenshot shows the detailed settings associated with the created Amazon SageMaker model resource.

Deploying the MOJO model to an auto-scaling Amazon SageMaker model endpoint

To deploy your MOJO model, complete the following steps:

On the Step Functions console, navigate to ModelTuningWithEndpointDeploymentStateMachine to find it in Running state.
Observe the ongoing Deploy Auto-scaling Model Endpoint step.

The following screenshot shows the Amazon SageMaker model endpoint during the deployment.

Auto-scaling model endpoint deployment takes approximately 5–6 minutes. When the endpoint is deployed, the Step Functions workflow successfully concludes.

Navigate to the model endpoint that is in InService status; it’s now ready to accept incoming requests.

Drill down to the model endpoint details and observe the endpoint runtime settings.

This model endpoint can scale from one to four instances, which are all behind Amazon SageMaker Runtime.

Testing the Amazon SageMaker model endpoint

For Window users, enter the following code to invoke the model endpoint:

aws sagemaker-runtime invoke-endpoint --endpoint-name survival-endpoint ^
--content-type application/jsonlines ^
--accept application/jsonlines ^
--body "{\"Pclass\":\"3\",\"Sex\":\"male\",\"Age\":\"22\",\"SibSp\":\"1\",\"Parch\":\"0\",\"Fare\":\"7.25\",\"Embarked\":\"S\"}"  response.json && cat response.json

For Linux and macOS users, enter the following code to invoke the model endpoint:

aws sagemaker-runtime invoke-endpoint --endpoint-name survival-endpoint \
--content-type application/jsonlines \
--accept application/jsonlines \
--body "{\"Pclass\":\"3\",\"Sex\":\"male\",\"Age\":\"22\",\"SibSp\":\"1\",\"Parch\":\"0\",\"Fare\":\"7.25\",\"Embarked\":\"S\"}"  response.json --cli-binary-format raw-in-base64-out && cat response.json

As displayed in the following model endpoint response, this unfortunate third-class male passenger didn’t survive (prediction is 0) according to the trained model:

{"calibratedClassProbabilities":"null","classProbabilities":"[0.686304913500942, 0.313695086499058]","prediction":"0","predictionIndex":0}

The invocation round-trip latency might be higher in the first call, but it decreases in the subsequent calls. This latency measurement from your PC to the Amazon SageMaker model endpoint also involves the network overhead of the local PC to AWS Cloud connection. To have an objective evaluation of model invocation performance, a load test based on real-life traffic expectations is essential.

Cleaning up

To stop incurring costs to your AWS account, delete the resources created in this post. For instructions, see Cleanup.

Conclusion

In this post, I explained how to use Amazon SageMaker to train and serve models for an H2O framework in a production-scale design pattern. This approach uses custom containers running a model training application built with a data science scripting language and a separate model hosting application built with a low-level language like Java, and has proven to be very robust and repeatable. You could also adapt this design pattern and its artifacts to other ML use cases.

About the Author

As a Machine Learning Prototyping Architect, Anil Sener builds prototypes on Machine Learning, Big Data Analytics, and Data Streaming, which accelerates the production journey on AWS for top EMEA customers. He has two masters degrees in MIS and Data Science.