Tag Archives: Amazon Cognito

In Case You Missed These: AWS Security Blog Posts from June, July, and August

Post Syndicated from Craig Liebendorfer original https://blogs.aws.amazon.com/security/post/Tx3KVD6T490MM47/In-Case-You-Missed-These-AWS-Security-Blog-Posts-from-June-July-and-August

In case you missed any AWS Security Blog posts from June, July, and August, they are summarized and linked to below. The posts are shown in reverse chronological order (most recent first), and the subject matter ranges from a tagging limit increase to recording SSH sessions established through a bastion host.

August

August 16: Updated Whitepaper Available: AWS Best Practices for DDoS Resiliency
We recently released the 2016 version of the AWS Best Practices for DDoS Resiliency Whitepaper, which can be helpful if you have public-facing endpoints that might attract unwanted distributed denial of service (DDoS) activity.

August 15: Now Organize Your AWS Resources by Using up to 50 Tags per Resource
Tagging AWS resources simplifies the way you organize and discover resources, allocate costs, and control resource access across services. Many of you have told us that as the number of applications, teams, and projects running on AWS increases, you need more than 10 tags per resource. Based on this feedback, we now support up to 50 tags per resource. You do not need to take additional action—you can begin applying as many as 50 tags per resource today.

August 11: New! Import Your Own Keys into AWS Key Management Service
Today, we are happy to announce the launch of the new import key feature that enables you to import keys from your own key management infrastructure (KMI) into AWS Key Management Service (KMS). After you have exported keys from your existing systems and imported them into KMS, you can use them in all KMS-integrated AWS services and custom applications.

August 2: Customer Update: Amazon Web Services and the EU-US Privacy Shield
Recently, the European Commission and the US Government agreed on a new framework called the EU-US Privacy Shield, and on July 12, the European Commission formally adopted it. AWS welcomes this new framework for transatlantic data flow. As the EU-US Privacy Shield replaces Safe Harbor, we understand many of our customers have questions about what this means for them. The security of our customers’ data is our number one priority, so I wanted to take a few moments to explain what this all means.

August 2: How to Remove Single Points of Failure by Using a High-Availability Partition Group in Your AWS CloudHSM Environment
In this post, I will walk you through steps to remove single points of failure in your AWS CloudHSM environment by setting up a high-availability (HA) partition group. Single points of failure occur when a single CloudHSM device fails in a non-HA configuration, which can result in the permanent loss of keys and data. The HA partition group, however, allows for one or more CloudHSM devices to fail, while still keeping your environment operational.

July

July 28: Enable Your Federated Users to Work in the AWS Management Console for up to 12 Hours
AWS Identity and Access Management (IAM) supports identity federation, which enables external identities, such as users in your corporate directory, to sign in to the AWS Management Console via single sign-on (SSO). Now with a small configuration change, your AWS administrators can allow your federated users to work in the AWS Management Console for up to 12 hours, instead of having to reauthenticate every 60 minutes. In addition, administrators can now revoke active federated user sessions. In this blog post, I will show how to configure the console session duration for two common federation use cases: using Security Assertion Markup Language (SAML) 2.0 and using a custom federation broker that leverages the sts:AssumeRole* APIs (see this downloadable sample of a federation proxy). I will wrap up this post with a walkthrough of the new session revocation process.

July 28: Amazon Cognito Your User Pools is Now Generally Available
Amazon Cognito makes it easy for developers to add sign-up, sign-in, and enhanced security functionality to mobile and web apps. With Amazon Cognito Your User Pools, you get a simple, fully managed service for creating and maintaining your own user directory that can scale to hundreds of millions of users.

July 27: How to Audit Cross-Account Roles Using AWS CloudTrail and Amazon CloudWatch Events
In this blog post, I will walk through the process of auditing access across AWS accounts by a cross-account role. This process links API calls that assume a role in one account to resource-related API calls in a different account. To develop this process, I will use AWS CloudTrail, Amazon CloudWatch Events, and AWS Lambda functions. When complete, the process will provide a full audit chain from end user to resource access across separate AWS accounts.

July 25: AWS Becomes First Cloud Service Provider to Adopt New PCI DSS 3.2
We are happy to announce the availability of the Amazon Web Services PCI DSS 3.2 Compliance Package for the 2016/2017 cycle. AWS is the first cloud service provider (CSP) to successfully complete the assessment against the newly released PCI Data Security Standard (PCI DSS) version 3.2, 18 months in advance of the mandatory February 1, 2018, deadline. The AWS Attestation of Compliance (AOC), available upon request, now features 26 PCI DSS certified services, including the latest additions of Amazon EC2 Container Service (ECS), AWS Config, and AWS WAF (a web application firewall). We at AWS are committed to this international information security and compliance program, and adopting the new standard as early as possible once again demonstrates our commitment to information security as our highest priority. Our customers (and customers of our customers) can operate confidently as they store and process credit card information (and any other sensitive data) in the cloud knowing that AWS products and services are tested against the latest and most mature set of PCI compliance requirements.

July 20: New AWS Compute Blog Post: Help Secure Container-Enabled Applications with IAM Roles for ECS Tasks
Amazon EC2 Container Service (ECS) now allows you to specify an IAM role that can be used by the containers in an ECS task, as a new AWS Compute Blog post explains. 

July 14: New Whitepaper Now Available: The Security Perspective of the AWS Cloud Adoption Framework
Today, AWS released the Security Perspective of the AWS Cloud Adoption Framework (AWS CAF). The AWS CAF provides a framework to help you structure and plan your cloud adoption journey, and build a comprehensive approach to cloud computing throughout the IT lifecycle. The framework provides seven specific areas of focus or Perspectives: business, platform, maturity, people, process, operations, and security.

July 14: New Amazon Inspector Blog Post on the AWS Blog
On the AWS Blog yesterday, Jeff Barr published a new security-related blog post written by AWS Principal Security Engineer Eric Fitzgerald. Here’s the beginning of the post, which is entitled, Scale Your Security Vulnerability Testing with Amazon Inspector:

July 12: How to Use AWS CloudFormation to Automate Your AWS WAF Configuration with Example Rules and Match Conditions
We recently announced AWS CloudFormation support for all current features of AWS WAF. This enables you to leverage CloudFormation templates to configure, customize, and test AWS WAF settings across all your web applications. Using CloudFormation templates can help you reduce the time required to configure AWS WAF. In this blog post, I will show you how to use CloudFormation to automate your AWS WAF configuration with example rules and match conditions.

July 11: How to Restrict Amazon S3 Bucket Access to a Specific IAM Role
In this blog post, I show how you can restrict S3 bucket access to a specific IAM role or user within an account using Conditions instead of with the NotPrincipal element. Even if another user in the same account has an Admin policy or a policy with s3:*, they will be denied if they are not explicitly listed. You can use this approach, for example, to configure a bucket for access by instances within an Auto Scaling group. You can also use this approach to limit access to a bucket with a high-level security need.

July 7: How to Use SAML to Automatically Direct Federated Users to a Specific AWS Management Console Page
In this blog post, I will show you how to create a deep link for federated users via the SAML 2.0 RelayState parameter in Active Directory Federation Services (AD FS). By using a deep link, your users will go directly to the specified console page without additional navigation.

July 6: How to Prevent Uploads of Unencrypted Objects to Amazon S3
In this blog post, I will show you how to create an S3 bucket policy that prevents users from uploading unencrypted objects, unless they are using server-side encryption with S3–managed encryption keys (SSE-S3) or server-side encryption with AWS KMS–managed keys (SSE-KMS).

June

June 30: The Top 20 AWS IAM Documentation Pages so Far This Year
The following 20 pages have been the most viewed AWS Identity and Access Management (IAM) documentation pages so far this year. I have included a brief description with each link to give you a clearer idea of what each page covers. Use this list to see what other people have been viewing and perhaps to pique your own interest about a topic you’ve been meaning to research. 

June 29: The Most Viewed AWS Security Blog Posts so Far in 2016
The following 10 posts are the most viewed AWS Security Blog posts that we published during the first six months of this year. You can use this list as a guide to catch up on your blog reading or even read a post again that you found particularly useful.

June 25: AWS Earns Department of Defense Impact Level 4 Provisional Authorization
I am pleased to share that, for our AWS GovCloud (US) Region, AWS has received a Defense Information Systems Agency (DISA) Provisional Authorization (PA) at Impact Level 4 (IL4). This will allow Department of Defense (DoD) agencies to use the AWS Cloud for production workloads with export-controlled data, privacy information, and protected health information as well as other controlled unclassified information. This new authorization continues to demonstrate our advanced work in the public sector space; you might recall AWS was the first cloud service provider to obtain an Impact Level 4 PA in August 2014, paving the way for DoD pilot workloads and applications in the cloud. Additionally, we recently achieved a FedRAMP High provisional Authorization to Operate (P-ATO) from the Joint Authorization Board (JAB), also for AWS GovCloud (US), and today’s announcement allows DoD mission owners to continue to leverage AWS for critical production applications.

June 23: AWS re:Invent 2016 Registration Is Now Open
Register now for the fifth annual AWS re:Invent, the largest gathering of the global cloud computing community. Join us in Las Vegas for opportunities to connect, collaborate, and learn about AWS solutions. This year we are offering all-new technical deep-dives on topics such as security, IoT, serverless computing, and containers. We are also delivering more than 400 sessions, more hands-on labs, bootcamps, and opportunities for one-on-one engagements with AWS experts.

June 23: AWS Achieves FedRAMP High JAB Provisional Authorization
We are pleased to announce that AWS has received a FedRAMP High JAB Provisional Authorization to Operate (P-ATO) from the Joint Authorization Board (JAB) for the AWS GovCloud (US) Region. The new Federal Risk and Authorization Management Program (FedRAMP) High JAB Provisional Authorization is mapped to more than 400 National Institute of Standards and Technology (NIST) security controls. This P-ATO recognizes AWS GovCloud (US) as a secure environment on which to run highly sensitive government workloads, including Personally Identifiable Information (PII), sensitive patient records, financial data, law enforcement data, and other Controlled Unclassified Information (CUI).

June 22: AWS IAM Service Last Accessed Data Now Available for South America (Sao Paulo) and Asia Pacific (Seoul) Regions
In December, AWS IAM released service last accessed data, which helps you identify overly permissive policies attached to an IAM entity (a user, group, or role). Today, we have extended service last accessed data to support two additional regions: South America (Sao Paulo) and Asia Pacific (Seoul). With this release, you can now view the date when an IAM entity last accessed an AWS service in these two regions. You can use this information to identify unnecessary permissions and update policies to remove access to unused services.

June 20: New Twitter Handle Now Live: @AWSSecurityInfo
Today, we launched a new Twitter handle: @AWSSecurityInfo. The purpose of this new handle is to share security bulletins, security whitepapers, compliance news and information, and other AWS security-related and compliance-related information. The scope of this handle is broader than that of @AWSIdentity, which focuses primarily on Security Blog posts. However, feel free to follow both handles!

June 15: Announcing Two New AWS Quick Start Reference Deployments for Compliance
As part of the Professional Services Enterprise Accelerator – Compliance program, AWS has published two new Quick Start reference deployments to assist federal government customers and others who need to meet National Institute of Standards and Technology (NIST) SP 800-53 (Revision 4) security control requirements, including those at the high-impact level. The new Quick Starts are AWS Enterprise Accelerator – Compliance: NIST-based Assurance Frameworks and AWS Enterprise Accelerator – Compliance: Standardized Architecture for NIST High-Impact Controls Featuring Trend Micro Deep Security. These Quick Starts address many of the NIST controls at the infrastructure layer. Furthermore, for systems categorized as high impact, AWS has worked with Trend Micro to incorporate its Deep Security product into a Quick Start deployment in order to address many additional high-impact controls at the workload layer (app, data, and operating system). In addition, we have worked with Telos Corporation to populate security control implementation details for each of these Quick Starts into the Xacta product suite for customers who rely upon that suite for governance, risk, and compliance workflows.

June 14: Now Available: Get Even More Details from Service Last Accessed Data
In December, AWS IAM released service last accessed data, which shows the time when an IAM entity (a user, group, or role) last accessed an AWS service. This provided a powerful tool to help you grant least privilege permissions. Starting today, it’s easier to identify where you can reduce permissions based on additional service last accessed data.

June 14: How to Record SSH Sessions Established Through a Bastion Host
A bastion host is a server whose purpose is to provide access to a private network from an external network, such as the Internet. Because of its exposure to potential attack, a bastion host must minimize the chances of penetration. For example, you can use a bastion host to mitigate the risk of allowing SSH connections from an external network to the Linux instances launched in a private subnet of your Amazon Virtual Private Cloud (VPC). In this blog post, I will show you how to leverage a bastion host to record all SSH sessions established with Linux instances. Recording SSH sessions enables auditing and can help in your efforts to comply with regulatory requirements.

June 14: AWS Granted Authority to Operate for Department of Commerce and NOAA
AWS already has a number of federal agencies onboarded to the cloud, including the Department of Energy, The Department of the Interior, and NASA. Today we are pleased to announce the addition of two more ATOs (authority to operate) for the Department of Commerce (DOC) and the National Oceanic and Atmospheric Administration (NOAA). Specifically, the DOC will be utilizing AWS for their Commerce Data Service, and NOAA will be leveraging the cloud for their “Big Data Project." According to NOAA, the goal of the Big Data Project is to “create a sustainable, market-driven ecosystem that lowers the cost barrier to data publication. This project will create a new economic space for growth and job creation while providing the public far greater access to the data created with its tax dollars.”

June 2: How to Set Up DNS Resolution Between On-Premises Networks and AWS by Using Unbound
In previous AWS Security Blog posts, Drew Dennis covered two options for establishing DNS connectivity between your on-premises networks and your Amazon Virtual Private Cloud (Amazon VPC) environments. His first post explained how to use Simple AD to forward DNS requests originating from on-premises networks to an Amazon Route 53 private hosted zone. His second post showed how you can use Microsoft Active Directory (also provisioned with AWS Directory Service) to provide the same DNS resolution with some additional forwarding capabilities. In this post, I will explain how you can set up DNS resolution between your on-premises DNS with Amazon VPC by using Unbound, an open-source, recursive DNS resolver. This solution is not a managed solution like Microsoft AD and Simple AD, but it does provide the ability to route DNS requests between on-premises environments and an Amazon VPC–provided DNS.

June 1: How to Manage Secrets for Amazon EC2 Container Service–Based Applications by Using Amazon S3 and Docker
In this blog post, I will show you how to store secrets on Amazon S3, and use AWS IAM roles to grant access to those stored secrets using an example WordPress application deployed as a Docker image using ECS. Using IAM roles means that developers and operations staff do not have the credentials to access secrets. Only the application and staff who are responsible for managing the secrets can access them. The deployment model for ECS ensures that tasks are run on dedicated EC2 instances for the same AWS account and are not shared between customers, which gives sufficient isolation between different container environments.

If you have comments  about any of these posts, please add your comments in the "Comments" section of the appropriate post. If you have questions about or issues implementing the solutions in any of these posts, please start a new thread on the AWS IAM forum.

– Craig

Readmission Prediction Through Patient Risk Stratification Using Amazon Machine Learning

Post Syndicated from Ujjwal Ratan original https://blogs.aws.amazon.com/bigdata/post/Tx1Z7AR9QTXIWA1/Readmission-Prediction-Through-Patient-Risk-Stratification-Using-Amazon-Machine

Ujjwal Ratan is a Solutions Architect with Amazon Web Services

The Hospital Readmission Reduction Program (HRRP) was included as part of the Affordable Care Act to improve quality of care and lower healthcare spending. A patient visit to a hospital may be constituted as a readmission if the patient in question is admitted to a hospital within 30 days after being discharged from an earlier hospital stay. This should be easy to measure right? Wrong.

Unfortunately, it gets more complicated than this. Not all readmissions can be prevented, as some of them are part of an overall care plan for the patient. There are also factors beyond the hospital’s control that may cause a readmission. The Center for Medicare and Medicaid Services (CMS) recognized the complexities with measuring readmission rates and came up with a set of measures to evaluate providers.

There is still a long way to go for hospitals to be effective in preventing unplanned readmissions. Recognizing factors effecting readmissions is an important first step, but it is also important to draw out patterns in readmission data by aggregating information from multiple clinical and non-clinical hospital systems.

Moreover, most analysis algorithms rely on financial data which omit the clinical nuances applicable to a readmission pattern. The data sets contain a lot of redundant information like patient demographics and historical data. All this creates a massive data analysis challenge that may take months to solve using conventional means.

In this post, I show how to apply advanced analytics concepts like pattern analysis and machine learning to do risk stratification for patient cohorts.

The role of Amazon ML

There have been multiple global scientific studies on scalable models for predicting readmissions with high accuracy. Some of them, like comparison of models for predicting early hospital readmissions and predicting hospital readmissions in the Medicare population, are great examples.

Readmission records demonstrate patterns in data that can be used in a prediction algorithm. These patterns can be separated as outliers that are used to identify patient cohorts with high risk. Attribute correlation helps to identify the significant features that effect readmission risk in a patient.  This risk stratification in patients is enabled by categorizing patient attributes into numerical, categorical, and text attributes and applying statistical methods like standard deviation, median analysis, and the chi-squared test. These data sets are used to build statistical models to identify patients demonstrating certain characteristics consistent with readmissions so necessary steps can be taken to prevent it.

Amazon Machine Learning (Amazon ML) provides visual tools and wizards that guide users in creating complex ML models in minutes. You can also interact with it using the AWS CLI and API to integrate the power of ML with other applications. Based on the chosen target attribute in Amazon ML, you can build ML models like a binary classification model that predicts between states of 0 or 1 or a numeric regression model that predicts numerical values based on certain correlated attributes.

Creating an ML model for readmission prediction

The following diagram represents a reference architecture for building a scalable ML platform on AWS.

  1. The first step is to get the data into Amazon S3, the object storage service from AWS.
  2. Amazon Redshift acts as the database for the huge amounts of structured clinical data. The data is loaded into Amazon Redshift tables and is massaged to make it more meaningful as a data source for an ML model.
  3. A binary classification ML model is created using Amazon ML, with Amazon Redshift as the data source. A real-time endpoint is also created to allow real-time querying for the ML model.
  4. Amazon Cognito is used for secure federated access to the Amazon ML real-time endpoint.
  5. A static web site is created on S3. This website hosts the end user facing application using which one can query the Amazon ML endpoint in real time.

The architecture above is just one of the ways in which you can use AWS for building machine learning applications. You can vary this architecture and add services such as Amazon Elastic Map Reduce (EMR) if your use case involves large volumes of unstructured data sets or build a business intelligence (BI) reporting interface for analysis of predicted metrics. AWS provides a range of services that act as building blocks for the use case you want to build.

 

Prerequisite: Start with a data set

The first step in creating an accurate model is to choose the right data set to build and train the model. For the purposes of this post, I am using a publicly available diabetes data set from the University of California, Irvine (UCI).  The data set consists of 101,766 rows and represents 10 years of clinical care records from 130 US hospitals and integrated delivery networks. It includes over 50 features (attributes) representing patient and hospital outcomes. The data set can be downloaded from the UCI website. The hosted zip file consists of two csv files. The first file, diabetic_data.csv, is the actual data set and the second file, IDs_mapping.csv is the master data for admission_type_id, discharge_disposition_id, and admission_source_id.

Amazon ML automatically splits source data sets into two parts. The first part is used to train the ML model and the second part is used to evaluate the ML model’s accuracy. In this case, seventy percent of the source data is used to train the ML model and thirty percent is used to evaluate it. This is represented in the data rearrangement attribute as shown below:

ML model training data set:

{
  "splitting": {
    "percentBegin": 0,
    "percentEnd": 70,
    "strategy": "random",
    "complement": false,
    "strategyParams": {
      "randomSeed": ""
    }
  }
}

ML model evaluation data set:

{
  "splitting": {
    "percentBegin": 70,
    "percentEnd": 100,
    "strategy": "random",
    "complement": false,
    "strategyParams": {
      "randomSeed": ""
    }
  }
}

The accuracy of ML models becomes better when more data is used to train it. The data set I’m using in this post is very limited for building a comprehensive ML model but this methodology can be replicated with larger data sets.

 

Prepare the data and move it into Amazon S3

For an ML model to be effective, you should prepare the data so that it provides the right patterns to the model. The data set should have good coverage for relevant features, be low in unwanted “noise” or variance, and be as complete as possible with correct labels.

Use the Amazon Redshift database to prepare the data set. To begin, copy the data into an S3 bucket named diabetesdata. The bucket consists of four CSV files:

You can LIST the bucket contents by running the following command in the AWS CLI:

aws s3 ls s3://diabetesdata

Following this, create the necessary tables in Amazon Redshift to process the data in the CSV files by creating three master tables in one transaction table.

The transaction table consists of lookup IDs which act as foreign keys (FK) from the above master tables. It also has a primary key “encounter_id” and multiple columns that act as features for the ML model. The createredshifttables.sql script is executed to create the above tables.         

After the necessary tables are created, start loading them with data. You can make use of the Amazon Redshift COPY command to copy the data from the files on S3 into the respective Amazon Redshift tables. The following script template details the format of the copy command used:

COPY diabetes_data from 's3://<S3 file path>' credentials 'aws_access_key_id=<AWS Access Key ID>;aws_secret_access_key=<AWS Secret Access Key>' delimiter ',' IGNOREHEADER 1;

The loaddata.sql script is executed for the data loading step.

 

Modify the data set in Amazon Redshift

The next step is to make some changes to the data set to make it less noisy and suitable for the ML model that you create later. There are various things you can do as part of this clean up, such as updating incomplete values and grouping attributes into categories. For example, age can be grouped into young, adult or old based on age ranges.

For the target attribute for your ML model, create a custom attribute called readmission_result, with a value of “Yes” or “No” based on conditions in the readmitted attribute. To see all the changes made to the data, see the ModifyData.sql script.

Finally, the complete modified data set is dumped into a new table, diabetes_data_modified, which acts as a source for the ML model. Notice the new custom column readmission_result, which is your target attribute for the ML model.

 

Create a data source for Amazon ML and build the ML model

Next, create an Amazon ML data source, choosing Amazon Redshift as the source. This can be easily done through the console or through the CreateDataSourceFromRedshift API operation by specifying the Redshift parameters like Cluster Name, Database Name, username, password, role and the SQL query. The IAM role for Amazon Redshift as a data source is easily populated, as shown in the screenshot below.

You need the entire data set for the ML model, so use the following query for the data source:

SELECT * FROM diabetes_data_modified

This can be modified with column names and WHERE clauses to build different data sets for training the ML model.

The steps to create a binary classification ML model are covered in detail in the Building a Binary Classification Model with Amazon Machine Learning and Amazon Redshift blog post.

Amazon ML provides two types of predictions that you can try. The first one is a batch prediction that can be generated through the console or the GetBatchPrediction API operation. The result of the batch prediction is stored in an Amazon S3 bucket and can be used to build reports for end users (like monthly actual value vs predicted value report).

You can also use the ML model to generate a real-time prediction. To enable real-time predictions, create an endpoint for the ML model either through the console or using the CreateRealTimeEndpoint API operation.

After it’s created, you can query this endpoint in real time to get a response from Amazon ML, as shown in the following CLI screenshot.


 

 

Build the end user application

The Amazon ML endpoint created earlier can be invoked using an API call. This is very handy for building an application for end users who can interact with the ML model in real time.

Create a similar application and host it as a static website on Amazon S3. This feature of S3 allows you to host websites without any web servers and takes away the complexities of scaling hardware based on traffic routed to your application. The following is a screenshot from the application:

The application allows end users to select certain patient parameters and then makes a call to the predict API. The results are displayed in real time in the results pane.

I made use of the AWS SDK for JavaScript to build this application. The SDK can be added to your script using the following code:

<script src="https://sdk.amazonaws.com/js/aws-sdk-2.3.3.min.js"></script>

 

Use Amazon Cognito for secure access

To authenticate the Amazon ML API request, you can make use of Amazon Cognito, which allows for secure access to the Amazon ML endpoint without making use of the AWS security credentials. To enable this, create an identity pool in Amazon Cognito.

Amazon Cognito creates a new role in IAM. You need to allow this new IAM role to interact with Amazon ML by attaching the AmazonMachineLearningRealTimePredictionOnlyAccess policy to the role. This IAM policy allows the application to query the Amazon ML endpoint.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "machinelearning:Predict"
      ],
      "Resource": "*"
    }
  ]
}

Next, initialize credential objects, as shown in the code below:

var parameters = {
      AccountId: "AWS Account ID",
      RoleArn: "ARN for the role created by Amazon Cognito",
      IdentityPoolId: "The identity pool ID created in Amazon Cognito"
       };
 // set the Amazon Cognito region
       AWS.config.region = 'us-east-1';
// initialize the Credentials object with the parameters
 AWS.config.credentials = new AWS.CognitoIdentityCredentials(parameters);

 

Call the AML Endpoint using the API

Create the function callApi() to make a call to the Amazon ML endpoint. The steps in the callAPI() function involve building the object that forms a part of the parameters sent to the Amazon ML endpoint, as shown in the code below:

var machinelearning = new AWS.MachineLearning({apiVersion: '2014-12-12'});
var params = {
	 	 	MLModelId: ‘<ML model ID>',
	  		PredictEndpoint: ‘<ML model real-time endpoint>',
	  		Record: 
		};
		var request = machinelearning.predict(params);

The API call returns a JSON object that includes, among other things, the predictedLabel and predictedScores parameters, as shown in the code below:

{
    "Prediction": {
        "details": {
            "Algorithm": "SGD",
            "PredictiveModelType": "BINARY"
        },
        "predictedLabel": "1",
        "predictedScores": {
            "1": 0.5548262000083923
        }
    }
}

The predictedScores parameter generates a score between 0 and 1 which you can convert into a percentage:

if(!isNaN(predictedScore)){
			finalScore = Math.round(predictedScore * 100);
			resultMessage = finalScore + "%";
		}

The complete code for this sample application is uploaded to PredictReadmission_AML GitHub repo for reference and can be used to create more sophisticated machine learning applications using Amazon ML.

 

Conclusion

The power of machine learning opens new avenues for advanced analytics in healthcare. With new means of gathering data that range from sensors mounted on medical devices to medical images and everything in between, the complexities demonstrated by these varied data sets are pushing the boundaries of conventional analysis techniques.

The advent of cloud computing has made it possible for researchers to take up the challenging task of synthesizing these data sets and draw insights that are providing us with information that we never knew existed.

We are still at the beginning of this journey and there are, of course, challenges that we have to overcome. The ease of availability of quality data sets, which is the starting point of any good analysis, is still a major hurdle. Regulations like Health Insurance Portability and Accountability Act of 1996 (HIPAA) make it difficult to obtain medical records with Protected Health Information (PHI). The good news is that this is changing with initiatives like AWS Public Data Sets, which hosts a variety of public data sets that anyone can use.

At the end of the day, all this analysis and research is for one cause: To improve the quality of human lives. I hope this is, and will continue to be, the greatest motivation to overcome any challenge.

If you have any questions or suggestions, please comment below.
_ _ _ _ _

Do you want to be part of the conversation? Join AWS developers, enthusiasts, and healthcare professionals as we discuss building smart healthcare applications on AWS in Seattle on August 31.

Seattle AWS Big Data Meetup (Wednesday, August 31, 2016)


 

Related

Building a Multi-Class ML Model with Amazon Machine Learning
 

 

Amazon Cognito Your User Pools is Now Generally Available

Post Syndicated from Vikram Madan original https://blogs.aws.amazon.com/security/post/Tx13NVD4AWG9QK9/Amazon-Cognito-Your-User-Pools-is-Now-Generally-Available

Amazon Cognito makes it easy for developers to add sign-up, sign-in, and enhanced security functionality to mobile and web apps. With Amazon Cognito Your User Pools, you get a simple, fully managed service for creating and maintaining your own user directory that can scale to hundreds of millions of users.

With today’s launch, user pools adds:

  • Device remembering – Amazon Cognito can remember the devices from which each user signs in.
  • User search – Search for users in a user pool based on an attribute.
  • Customizable email addresses – Customize the "from" email address of emails you send to users in a user pool.
  • Attribute permissions – Set fine-grained permissions for each user attribute.
  • Custom authentication flow – Use new APIs and AWS Lambda triggers to customize the sign-in flow.
  • Admin sign-in – Your app can now sign in users from back-end servers or Lambda functions. 
  • Global sign-out – Allow a user to sign out from all signed-in devices or browsers.
  • Custom expiration period – Set an expiration period for refresh tokens.
  • Amazon API Gateway integration – Allow user pool authentications to authorize Amazon API Gateway requests.

You benefit from the security and privacy best practices of AWS, and retain full control of your user data.

Amazon Cognito is now also available in the US West (Oregon) Region in addition to the US East (N. Virginia), Asia Pacific (Tokyo), and EU (Ireland) Regions. To begin using this new feature of Amazon Cognito, see the Amazon Cognito page.

To learn more, see the AWS Blog and the related documentation.

– Vikram 

Amazon Cognito Your User Pools – Now Generally Available

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-cognito-your-user-pools-now-generally-available/

A few months ago I wrote about the new Your User Pools feature for Amazon Cognito. As I wrote at the time, you can use this feature to easily add user sign-up and sign-in to your mobile and web apps. The fully managed user directories can scale to hundreds of millions of users and you can have multiple directories per AWS account. Creating a user pool takes just a few minutes and you can decide exactly which attributes (address, email, gender, phone number, and so forth, plus custom attributes) must be entered when a new user signs up for your app or service. On the security side, you can specify the desired password strength, require the use of Multi-Factor Authentication (MFA), and verify new users via phone number or email address.

Now Generally Available
We launched Your User Pools as a public beta and received lots of great feedback. Today we are making Your User Pools generally available and we are also adding a large collection of new features:

  • Device Remembering – Cognito can remember the devices that each user signs in from.
  • User Search – Search for users in a user pool based on an attribute.
  • Customizable Email Addresses – Control the email addresses for emails to users in your user pool.
  • Attribute Permissions – Set fine-grained permissions for each user attribute.
  • Custom Authentication Flow – Use new APIs and Lambda triggers to customize the sign-in flow.
  • Admin Sign-in – Your app can now sign in users from backend servers or Lambda functions.
  • Global Sign-out – Allow a user to sign out from all signed-in devices or browsers.
  • Custom Expiration Period – Set an expiration period for refresh tokens.
  • API Gateway Integration – Use user pool to authorize Amazon API Gateway requests.
  • New Regions – Cognito Your User Pools are now available in additional AWS Regions.

Let’s take a closer look at each of these new features!

Device Remembering
Cognito can now remember the set of devices used by (signed in from) each user. You, as the creator of the user pool, have the option to allow your users to request this behavior. If you have enabled MFA for a user pool, you can also choose to eliminate the need for entry of an MFA code on a device that has been remembered. This simplifies and streamlines the login process on a remembered device, while still requiring entry of an MFA code for unrecognized devices. You can also list a user’s devices and allow them to sign out from a device remotely.

You can enable and customize this feature when you create a new user pool; you can also set it up for an existing pool. Here’s how you enable and customize it when you create a new user pool. First you enable the feature by clicking on Always or User Opt-in:

Then you indicate whether you would like to suppress MFA on remembered devices:

The AWS Mobile SDKs for iOS, Android, and JavaScript contain new methods that you can call from your app to remember devices.

User Search
You, as the creator of a Your User Pool, can now search for users based on a user attribute such as username, given_name, family_name, name, preferred_user_name, email, phone_number, status, or user_status.

You can do a full match or a prefix match using the AWS Management Console, the ListUsers API function, or the list-users command line tool. Here’s a Console-powered search:

Customizable Email Addresses
You can now specify the From and the Reply-To email addresses that are used to communicate with your users. Here’s how you specify the addresses when you create a new pool:

You will need to verify the From address with Amazon Simple Email Service (SES) before you can use it (read Verifying Email Addresses in Amazon SES to learn more).

Attribute Permissions
You can now set per-app read and write permissions for each user attribute. This gives you the ability to control which applications can see and/or modify each of the attributes that are stored for your users. For example, you could have a custom attribute that indicates whether a user is a paying customer or not. Your apps could see this attribute but could not modify it directly. Instead, you would update this attribute using an administrative tool or a background process. Permissions for user attributes can be set from the Console, the API, or the CLI.

Custom Authentication Flow
You can now use a pair of new API functions (InitiateAuth and RespondToAuthChallenge) and three new Lambda triggers to create your own sign-in flow or to customize the existing one. You can, for example, customize the user flows for users with different levels of experience, different locations, or different security requirements. You could require the use of a CAPTCHA for some users or for all users, as your needs dictate.

The new Lambda triggers are:

Define Auth Challenge – Invoked to initiate the custom authentication flow.

Create Auth Challenge – Invoked if a custom authentication challenge has been defined.

Verify Auth Challenge Response – Invoked to check the validity of a custom authentication challenge.

You can set up the triggers from the Console like this:

Global Sign-out
You can now give your users the option to sign out (by invalidating tokens) of all of the devices where they had been signed in. Apps can call the [GlobalSignOut] function using a valid, non-expired, non-revoked access token. Developers can remotely sign out any user by calling the [AdminUserGlobalSignOut] function using a Pool ID and a username.

Custom Expiration Period
Cognito sign-in makes use of “refresh” tokens to eliminate the need to sign in every time an application is opened. By default, the token expires after 30 days. In order to give you more control over the balance between security and convenience, you can now set a custom expiration period for the refresh tokens generated by each of your user pools.

API Gateway Integration
Cognito user pools can now work hand-in-hand with Amazon API Gateway to authorize API requests. You can configure API Gateway to accept Id tokens to authorize users based on their presence in a user pool.

To do this, you first create a Cognito User Pool Authorizer using the API Gateway Console, referencing the user pool and choosing the request header that will contain the identity token:

Navigate to the desired method and select the new Authorizer:

New Regions
As part of today’s launch we are making Cognito available in the US West (Oregon) Region.

In addition to the existing availability in the US East (Northern Virginia) Region, we are making Your User Pools available in the Europe (Ireland), US West (Oregon), and Asia Pacific (Tokyo) Regions.

Available Now
These new features are available now and you can start using them today!


Jeff;

In Case You Missed These: AWS Security Blog Posts from March and April

Post Syndicated from Craig Liebendorfer original https://blogs.aws.amazon.com/security/post/Tx3CTMUP8IVAQX0/In-Case-You-Missed-These-AWS-Security-Blog-Posts-from-March-and-April

In case you missed any of the AWS Security Blog posts from March and April, they are summarized and linked to below. The posts are shown in reverse chronological order (most recent first), and the subject matter ranges from the AWS Config Rules repository to automatically updating AWS WAF IP blacklists.

April

April 28, AWS WAF How-To: How to Import IP Address Reputation Lists to Automatically Update AWS WAF IP Blacklists
A number of organizations maintain reputation lists of IP addresses used by bad actors. Their goal is to help legitimate companies block access from specific IP addresses and protect their web applications from abuse. These downloadable, plaintext reputation lists include Spamhaus’s Don’t Route Or Peer (DROP) List and Extended Drop (EDROP) List, and Proofpoint’s Emerging Threats IP list. Similarly, the Tor project’s Tor exit node list provides a list of IP addresses currently used by Tor users to access the Internet. Tor is a web proxy that anonymizes web requests and is sometimes used by malicious users to probe or exploit websites.

April 27, Federated SSO How-To: How to Set Up Federated Single Sign-On to AWS Using Google Apps
Among the services offered to Google Apps for Work users is a Security Assertion Markup Language (SAML) 2.0–based SSO service. You can use this service to provide one-click SSO to your AWS resources by using your existing Google Apps credentials. For users to whom you grant SSO access, they will see an additional SAML app in your Google Apps account, as highlighted in the following screenshot. When your users click the SAML app, Google Apps authenticates and redirects them to the AWS Management Console. In this blog post, I will show you how you can use Google Apps to set up federated SSO to your AWS resources.

April 21, AWS WAF How-To: How to Prevent Hotlinking by Using AWS WAF, Amazon CloudFront, and Referer Checking
You can use AWS WAF to help prevent hotlinking. AWS WAF is a web application firewall that is closely integrated with Amazon CloudFront (AWS’s content delivery network [CDN]), and it can help protect your web applications from common web exploits that could affect application availability, compromise security, and consume excessive resources. In this blog post, I will show you how to prevent hotlinking by using header inspection in AWS WAF, while still taking advantage of the improved user experience from a CDN such as CloudFront.

April 20, Amazon Cognito Announcement: Amazon Cognito Now Provides Sign-Up and Sign-In Functionality for Your Apps (Beta)
Today, Amazon Cognito launched the beta of a new feature that makes it easy for developers to add sign-up and sign-in functionality to mobile and web apps. With this new feature, you get a simple, fully managed service you can use to create and maintain your user pool that can scale to hundreds of millions of users. This new feature also provides enhanced security functionality, such as email verification, phone number verification, and multi-factor authentication. You benefit from the security and privacy best practices of AWS, and retain full control of your user data. To begin using the new beta feature with your user pool, see the Amazon Cognito page.

April 20, Amazon Inspector Announcement: Now Generally Available: Amazon Inspector
Yesterday, AWS announced that Amazon Inspector, an automated security assessment service, is now available to all customers. Inspector helps you improve the security and compliance of your applications running on Amazon Elastic Compute Cloud (Amazon EC2) by identifying potential security issues, vulnerabilities, or deviations from security standards. You pay only for the assessments you run, with the first 250 assessments free for your first 90 days.

April 19, HIPAA FAQ: Frequently Asked Questions About HIPAA Compliance in the AWS Cloud
Today, we continue a series of AWS cloud compliance FAQs by focusing on the Health Insurance Portability and Accountability Act (HIPAA) and protected health information (PHI). AWS’s Healthcare and Life Science customers are doing important things for their customers in the AWS cloud, and we are excited to work with our partners to help tackle medical advancements at scale. In this blog post, I will share some of the broader questions we hear from customers about HIPAA compliance and PHI in the cloud.

April 14, RDS for SQL Server How-To: How to Enable Windows Integrated Authentication for RDS for SQL Server Using On-Premises Active Directory
If you want to run your SQL Server applications in AWS and secure access with on-premises Active Directory user accounts, this blog post is for you. In this blog post, I walk you through the steps to enable RDS for SQL Server to authenticate with Microsoft AD and configure trusts between Microsoft AD and your on-premises Active Directory. With that configuration in place, you can run your SQL Server databases and applications in AWS, and authenticate access with on-premises Active Directory user accounts.

April 7, AWS Directory Service Announcement: Now Available: Simplified Configuration of Trust Relationships in the AWS Directory Service Console
Today, we made it easier for you to configure trust relationships between AWS Directory Service for Microsoft Active Directory (Enterprise Edition), also referred to as Microsoft AD, and your on-premises Microsoft Active Directory. Establishing trust relationships requires conditional forwarders, which resolve Domain Name System (DNS) queries between the domain names of trusting directories. Now, by completing a single field in the Directory Service console at the same time you create a trust relationship, you can more easily configure conditional forwarders. 

April 7, Compliance FAQ: Frequently Asked Questions About Compliance in the AWS Cloud
Every month, AWS Compliance fields thousands of questions about how to achieve and maintain compliance in the cloud. Among other things, customers are eager to take advantage of the cost savings and security at scale that AWS offers while still maintaining robust security and regulatory compliance. Because regulations across industries and geographies can be complex, we thought it might be helpful to share answers to some of the frequently asked questions we hear about compliance in the AWS cloud, as well as to clear up potential misconceptions about how operating in the cloud might affect compliance.

March

March 29, Amazon CloudWatch Events How-To: How to Detect and Automatically Revoke Unintended IAM Access with Amazon CloudWatch Events
If your account is shared across departments in your organization, monitoring the permissions of your users can become a challenge as the number of users grows. For example, what if a user is granted unintended IAM API access and the user begins making API calls? In this post, I will show a solution that detects API callers who should not have IAM access and automatically revokes those permissions with the help of Amazon CloudWatch Events.

March 28, AWS CloudTrail How-To: How to Easily Identify Your Federated Users by Using AWS CloudTrail
CloudTrail now records two additional AWS Security Token Service (AWS STS) API calls: AssumeRoleWithWebIdentity and AssumeRoleWithSAML. If you already have CloudTrail logging enabled, capturing these AWS STS API calls is enabled by default and requires no additional action from you. If you have not enabled CloudTrail already, see the CloudTrail documentation and AWS CloudTrail FAQs for more information. In this blog post, I will show how you can identify a SAML federated user who terminated an EC2 instance in your AWS account.

March 23, AWS Webinar Announcement: Register for and Attend This March 30 Webinar—Best Practices for Managing Security Operations in AWS
AWS Security Solutions Architect Henrik Johansson will share different ways you can use AWS Identity and Access Management (IAM) to control access to your AWS services and integrate your existing authentication system with AWS IAM. You will learn how you can deploy and control your AWS infrastructure as code by using templates, including change management policies with AWS CloudFormation. In addition, you will explore different options for managing both your AWS access logs and your Amazon Elastic Compute Cloud (EC2) system logs using AWS CloudTrail and Amazon CloudWatch Logs. You will also learn how to implement an audit and compliance validation process using AWS Config and Amazon Inspector.

March 22, AWS Encryption SDK How-To: How to Use the New AWS Encryption SDK to Simplify Data Encryption and Improve Application Availability
The AWS Cryptography team is happy to announce the AWS Encryption SDK. This new SDK makes encryption easier for developers while minimizing errors that could lessen the security of your applications. The new SDK does not require you to be an AWS customer, but it does include ready-to-use examples for AWS customers.

March 16, AD FS Federation How-To: How to Set Up Uninterrupted, Federated User Access to AWS Using AD FS
When the token-signing certificate expires, or is changed, the trust relationship between the claim provider, AD FS, and the relying party, AWS Security Token Service (AWS STS), is broken. Without a valid certificate to prove the calling server’s identity, the receiving party cannot verify the certificate, which terminates the request and thus prevents federated users from being able to access the AWS Management Console. Luckily, this can be avoided! In this blog post, I explain how you can use the AutoCertificateRollover feature in AD FS to enable uninterrupted connections between your claim provider and your relying trust. I also show how to set up a secondary certificate manually in AD FS to avoid service interruption when a server certificate expires.

March 8, AWS WAF How-To: How to Reduce Security Threats and Operating Costs Using AWS WAF and Amazon CloudFront
Successfully blocking bad actors can help reduce security threats to your systems. In addition, you can lower your overall costs, because you no longer have to serve traffic to unintended audiences. In this blog post, I will show you how you can realize these benefits by building a process to help detect content scrapers and bad bots, and then use Amazon CloudFront with AWS WAF (a web application firewall [WAF]) to help block bad actors’ access to your content.

March 7, Restricting VPC Access How-To: How to Automate Restricting Access to a VPC by Using AWS IAM and AWS CloudFormation
Back in September, I wrote about How to Help Lock Down a User’s Amazon EC2 Capabilities to a Single VPC. In that blog post, I highlighted what I have found to be an effective approach to the virtual private cloud (VPC) lockdown scenario. Since that time, I have worked on making the related information easier to implement in your environment. As a result, I have developed an AWS CloudFormation template that automates the creation of the resources necessary to lock down AWS Identity and Access Management (IAM) entities (users, groups, and roles) to a VPC. In this blog post, I explain this CloudFormation template in detail and describe its individual sections in order to help you better understand what happens when you create a CloudFormation stack from the template.

March 1, AWS Config Rules Repository Announcement: Announcing the AWS Config Rules Repository: A New Community-Based Source of Custom Rules for AWS Config
Today, we’re happy to release the AWS Config Rules repository, a community-based source of custom AWS Config Rules. This new repository gives you a streamlined way to automate your assessment and compliance against best practices for security of AWS resources. AWS Config Rules is a service that provides automated, periodic security and compliance checking of AWS resources, and affords customers the ability to forego manual inspection of security configurations.

If you have comments  about any of these posts, please add your comments in the "Comments" section of the appropriate post. If you have questions about or issues implementing the solutions in any of these posts, please start a new thread on the AWS IAM forum.

– Craig

AWS Week in Review – April 18, 2016

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-week-in-review-april-18-2016/

Let’s take a quick look at what happened in AWS-land last week:

Monday

April 18

Tuesday

April 19

Wednesday

April 20

Thursday

April 21

Friday

April 22

Saturday

April 23

Sunday

April 24

New & Notable Open Source

New SlideShare Presentations

New Customer Success Stories

New YouTube Videos

Upcoming Events

Help Wanted

Stay tuned for next week! In the meantime, follow me on Twitter and subscribe to the RSS feed.

Jeff;

Amazon Cognito Now Provides Sign-Up and Sign-In Functionality for Your Apps (Beta)

Post Syndicated from Vikram Madan original https://blogs.aws.amazon.com/security/post/Tx33NCBHZ79R387/Amazon-Cognito-Now-Provides-Sign-Up-and-Sign-In-Functionality-for-Your-Apps-Beta

Today, Amazon Cognito launched the beta of a new feature that makes it easy for developers to add sign-up and sign-in functionality to mobile and web apps. With this new feature, you get a simple, fully managed service you can use to create and maintain your user pool that can scale to hundreds of millions of users. This new feature also provides enhanced security functionality, such as email verification, phone number verification, and multi-factor authentication. You benefit from the security and privacy best practices of AWS, and retain full control of your user data. To begin using the new beta feature with your user pool, see the Amazon Cognito page.

To learn more, see the AWS Blog, AWS Mobile Blog, and the related documentation.

– Vikram

New – Your User Pools for Amazon Cognito

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-user-pools-for-amazon-cognito/

Amazon Cognito makes it easy for mobile and web apps to easily add authentication, user management, and data synchronization without having to write backend code or manage any infrastructure. In the last year we have added some powerful new features to Cognito including AWS CloudTrail support, the use of Twitter and Digits as login providers, the ability to run AWS Lambda functions in response to events in Cognito, and the streaming of user identity data into Amazon Kinesis.

Your User Pools
You can now use Amazon Cognito to easily add user sign-up and sign-in to your mobile and web apps. With the user pools feature, you can create your own user directory that can scale to hundreds of millions of users, and is fully managed so you don’t have to worry about the heavy lifting associated with building, securing, and scaling authentication to your apps. This feature also provides enhanced security functionality such as email verification, phone number verification, and multi-factor authentication. As an app developer, you already had the option to use an external identity provider such as Amazon, Facebook, Google, Twitter or Digits for this purpose using the Cognito feature that we now call Federated Identity Pools.

Using a user pool gives you detailed control over the sign-up and sign-in aspects of your web and mobile SaaS apps, games, and so forth. Building and running a directory service at scale (potentially tens or even hundreds of millions of users) is not easy, but is definitely undifferentiated heavy lifting, with the added security burden that comes when you are managing user names, passwords, email addresses, and other sensitive pieces of information. You don’t need to build or run your own directory service when you use a Your User Identity Pool.

Multiple User Pools per Account
You can create multiple user pools within your AWS account (the identities in one pool are separate and distinct from those in any other pools). Each user pool is named, and you have full control over the attributes (address, email, phone number(s), locale, and so forth) that you want to store for each user. After you create a user pool, you can use the AWS Mobile SDKs (available for iOS, Android, and JavaScript) to authenticate users, get access credentials, and so forth.

Your users will benefit from a number of security features including SMS-based Multi-Factor Authentication (MFA) and account verification via phone or email. The password features use the Secure Remote Password (SRP) protocol to avoid sending cleartext passwords over the wire.

Creating a User Pool
Let’s walk through the process of creating a user pool from the AWS Management Console (API and CLI support is also available). My (hypothetical) app is called PaymentApp so I’ll create a pool named PaymentAppUsers:

I can either review and accept the defaults, or I can step through all of the settings. I’ll choose the latter course of action. Now I choose the set of attributes that must be collected when a new user signs up for my service:

I can also set up custom attributes. For my app, I would like to track the user’s preferred payment currency:

Then I set up the desired password strength. Since this is payment app, I’ll check all of the options:

Next, I enable Multi-Factor Authentication, and indicate that email addresses and phone numbers must be verified. I also customize the messages that are associated with each method of verification:

My app will have a mobile client so I’ll arrange to create a unique ID and a secret key for it:

Now I can arrange to have Cognito invoke some Lambda functions during the sign-up, verification, authentication, and confirmation steps (this is optional, but very useful if you want to customize the sign-up workflow by validating custom attributes):

Finally, I review my choices and create my pool:

At this point I am ready to build my web or mobile app.

Cognito at Asurion
Asurion provides over 200 million customers with insurance policies for high-value devices such as smartphones. Asurion plans to use Cognito to manage the user directory for their new device protection app. This app will collect device-related data and make recommendations that are aimed at optimizing usage.

Asurion chose Cognito due to its support for a wide variety of identity models. They can have their own fully managed directory or they can have users authenticate through third-party social identity providers without having to deal with the heavy lifting involved in scaling and security an identity system.

Ravi Tiyyagura (Asurion’s Director of Enterprise Architecture) told us:

It is critical for us to provide a secure and simple sign-up and sign-in experience for our tens of millions of end users. With Amazon Cognito, we can enable that without having to worry about building and managing any backend infrastructure.

Public Beta
We are launching user pools today as a public beta. All of the primary functionality is in place and you can use it to start building and testing your app. We expect to make it available for production use within a couple of months.


Jeff;