Category: Advanced (300)

AWS Single Sign-On (AWS SSO) is where you create or connect your workforce identities in AWS once and manage access centrally across your AWS Organization. Learn about the new integration with CyberArk Workforce Identity to provide simplified access management and provisioning to AWS. With this integration, you can have a single point of truth for all enterprise identities and enforce consistent management of users, groups, permissions, and access policies while reducing redundancies and errors.

There are multiple use cases where applications running on AWS may require a Lambda function to process the file stored in Box storage before it’s sent to the next stage for further processing. This post explains how a Lambda function can be invoked on the upload of a file in a Box folder using a webhook. With the help of a Box webhook and Amazon API Gateway, users can invoke a Lambda function on file upload operation and download the file to Amazon S3.

A serverless architecture strategy reduces complexity and provides more flexibility in adopting the features and non-functional requirements needed to support market agility. In this post, walk through an example of an IoT use case and build a serverless scalable platform using Amazon Kinesis Data Firehose, Amazon Managed Service for Apache Flink, and MongoDB Realm. You’ll learn how easy it is to develop mobile and desktop applications on top of the data platform for different personas.

Customer onboarding remains a challenging and time-consuming process for most banks. Both digital and traditional processes are often overly complex, resulting in lower conversion rates and higher cost of acquisition. To overcome these challenges, numerous financial institutions have started customer onboarding online. In this post, walk through the use case of one of the largest financial institutes of Europe for whom Infostretch provided a substantial breakthrough to onboard the customers digitally.

With the proliferation of different, constantly changing applications, it’s become a challenge to innovate quickly while having a strategy in place to prevent ballooning storage costs. Legacy archival and analytic tools often restrict developers and diminish the benefits of NoSQL’s handling of modern, rich data sources. Learn how MongoDB is committed to making it easier for customers to scale and transform their data management solutions to make developers’ lives easier.

Building effective machine learning models requires storing and managing historical data, but conventional databases can quickly become a nightmare to regulate. Queries start taking too long, for example, slowing down business decisions. Learn how to use Amazon Redshift ML and Query Editor V2 to create, train, and apply ML models to predict diabetes cases for a sample diabetes dataset. You can follow a similar approach to address other use cases such as customer churn prediction and fraud detection.

Securing the Kubernetes environment without compromising on developer agility holds the key to success with cloud-native adoption. The Nirmata Kubernetes Platform is designed to address these challenges comprehensively. It’s an open and easy-to-adopt platform to deploy, operate, and optimize Kubernetes workloads on any infrastructure, enabling self-service, segregation of responsibilities, and security and governance controls.

This post will show you how to send Amazon Inspector‘s Common Vulnerabilities and Exposures (CVE) findings to the ServiceNow Security Operations (SecOps) module. At the time of this write-up, there is no native adaptor from AWS to the ServiceNow SecOps module and, as such, requires a custom integration. Learn how to use ServiceNow’s native RESTful API to create a custom interface that an AWS Lambda function will leverage to send the CVE findings as they reach AWS Security Hub.

Operationalizing machine learning models can be a challenge due to lack of established ML architecture and its integration with the existing landscape. DataRobot integrates with AWS and provides the flexibility for a model trained in DataRobot to be deployed on AWS services with centralized model governance, management, and monitoring. Learn how the DataRobot AutoML platform orchestrates the complete model development and training lifecycle.

Some contact center platforms don’t offer native integrations with AWS AI/ML services. Consequently, customers must learn to live with a “just enough” contact center until their license agreements expire, look for other options beyond AWS, or wait for the platform provider to build the integrations in future releases. Learn how Universal Speech Solutions (also known as Unispeech) created AWS AI/ML plugins that enable the non-native integrations.