With growing needs for adopting cloud to speed up scientific research, higher-ed institutions are looking to democratize research with self-service portals for data science. To know more about how Relevance Lab is partnering with AWS Public sector group and some leading US universities to create a frictionless research platform leveraging open-source solutions click here. Click here for the full story.
Research computing is a growing need and AWS cloud enables researchers to process big data with scalable computing in a secure and flexible manner. While Cloud computing is a powerful platform it also brings in complexity with new tools, nomenclature and architectures that distract researchers to focus on infrastructure, security, governance, and costs. Relevance Lab is partnering with AWS Public sector group and some leading US universities to create a frictionless research platform leveraging open-source solutions.
Service Workbench from AWS is a powerful open-source solution for enabling research in cloud. Customers around the globe are already using this solution for common use cases.
While Service Workbench Current Version 5.2.1 provides a good foundation platform for research it also has some challenges based on feedback from early adopters mainly related to following:
To fix some of the known limitations of Service Workbench a new Version release is planned and available for preview. The primary benefits of this new release are following:
Functionality of Researcher Portal build on Service Workbench Open source
Working closely with AWS teams and a customer self-service Researcher Portal created with the primary goal to drive frictionless research in cloud with following key features:
The key functions flows needed for a Researcher are explained in the figure below:
Solution Architecture of Researcher Portal
The building blocks for the solution leverage the new Service Workbench release available with API only functionality and creates a separate Researcher Portal (RP) layer for providing a UI driven application to Researchers roles and a CLI interface for Admin users. The figure below captures the building blocks for this solution.
Deployment Architecture of Researcher Portal
The solution is deployed in an enterprise model for each customer in their AWS accounts and recommends the following architecture based on AWS Well Architected Framework as explained in figure below.
Sample screens for Researcher Portal
The key functionality for the solution is explained in some sample screens below.
An AIOps platform driven by Intelligent Automation paves the way for a faster and seamless Digital Transformation Journey. Click here for the full story.
With growing interest & investments in new concepts like Automation and Artificial Intelligence, the common dilemma for enterprises is how to scale these for significant impacts to their relevant context. It is easy to do a small proof of concept but much harder to make broader impacts across the landscape of Hybrid Infrastructure, Applications and Service Delivery models. Even more complex is Organizational Change Management for underlying processes, culture and “Way of Working”. There is no “Silver bullet” or “cookie-cutter” approach that can give radical changes, but it requires an investment in a roadmap of changes across People, Process and Technology. RLCatalyst solution from Relevance Lab provides an Open Architecture approach to interconnect various systems, applications, and processes like the “Enterprise Service Bus” model.
What is Intelligent Automation?
The key building blocks of automation depend on the concept of BOTs. So, what are BOTs?
To deploy BOTs across the enterprise and benefit from more sophisticated automation leveraging AI (Artificial Intelligence), RLCatalyst provides a prescriptive path to maturity as explained in the figure below.
ASB Approach
An Open- Architecture approach to interconnect various systems, applications, and processes similar to the “Enterprise Service Bus” model. This innovative approach of “software-defined” models, extendable meta-data for configurations, and a hybrid architecture takes into consideration modern distributed security needs. This ASB model helps to drive “Touchless Automation” with pre-built components and rapid adoption by existing enterprises.
To support a flexible deployment model that integrates with current SAAS (Software as a Service) based ITSM Platforms allows Automation to be managed securely inside Cloud or On-Premise data centers. The architecture supports a hybrid approach with multi-tenant components along with secure per instance-based BOT servers managing local security credentials. This comprehensive approach helps to scale Automation from silos to enterprise-wide benefits of human effort savings, faster velocity, better compliance and learning models for BOT efficiency improvements.
RLCatalyst provides solutions for enterprises to create their version of an Open Architecture based AIOps Platform that can integrate with their existing landscape and provide a roadmap for maturity.
Relevance Lab is working closely with leading enterprises from different verticals of Digital Learning, Health Sciences & Financial Asset Management in creating a common “Open Platform” that helps bring Automation-First approach and a maturity model to incrementally make Automation more “Intelligent”.
For more information feel free to contact marketing@relevancelab.com
References
Get Started with Building Your Automation Factory for Cloud
Intelligent Automation For User And Workspace Onboarding
Intelligent Automation with AS/400 based Legacy Systems support using UiPath
RLCatalyst BOTs Service Management connector for ServiceNow
Major advances are happening with the leverage of Cloud Technologies and large Open Data sets in the areas of Healthcare informatics that include sub-disciplines like Bioinformatics and Clinical Informatics. This is being rapidly adopted by Life Sciences and Healthcare institutions in commercial and public sector space. This domain has deep investments in scientific research and data analytics focussing on information, computation needs, and data acquisition techniques to optimize the acquisition, storage, retrieval, obfuscation, and secure use of information in health and biomedicine for evidence-based medicine and disease management.
In recent years, genomics and genetic data have emerged as an innovative areas of research that could potentially transform healthcare. The emerging trends are for personalized medicine, or precision medicine leveraging genomics. Early diagnosis of a disease can significantly increase the chances of successful treatment, and genomics can detect a disease long before symptoms present themselves. Many diseases, including cancers, are caused by alterations in our genes. Genomics can identify these alterations and search for them using an ever-growing number of genetic tests.
With AWS, genomics customers can dedicate more time and resources to science, speeding time to insights, achieving breakthrough research faster, and bringing life-saving products to market. AWS enables customers to innovate by making genomics data more accessible and useful. AWS delivers the breadth and depth of services to reduce the time between sequencing and interpretation, with secure and frictionless collaboration capabilities across multi-modal datasets. Also, you can choose the right tool for the job to get the best cost and performance at a global scale— accelerating the modern study of genomics.
Relevance Lab Research@Scale Architecture Blueprint
Working closely with AWS Healthcare and Clinical Informatics teams, Relevance Lab is bringing a scalable, secure, and compliant solution for enterprises to pursue Research@Scale on Cloud for intramural and extramural needs. The diagram below shows the architecture blueprint for Research@Scale. The solution offered on the AWS platform covers technology, solutions, and integrated services to help large enterprises manage research across global locations.
Leveraging AWS Biotech Blueprint with our Research Gateway
Use case with AWS Biotech Blueprint that provides a Core template for deploying a preclinical, cloud-based research infrastructure and optional informatics software on AWS.
This Quick Start sets up the following:
Using the Quickstart templates, the products were added to AWS Service Catalog and imported into RLCatalyst Research Gateway.
Using the standard products, the Nextflow Workflow Orchestration engine was launched for Genomics pipeline analysis. Nextflow helps to create and orchestrate analysis workflows and AWS Batch to run the workflow processes.
Nextflow is an open-source workflow framework and domain-specific language (DSL) for Linux, developed by the Comparative Bioinformatics group at the Barcelona Centre for Genomic Regulation (CRG). The tool enables you to create complex, data-intensive workflow pipeline scripts, and simplifies the implementation and deployment of genomics analysis workflows in the cloud.
This Quick Start sets up the following environment in a preclinical VPC:
RStudio for Scientific Research
RStudio is a popular IDE, licensed either commercially or under AGPLv3, for working with R. RStudio is available in a desktop version or a server version that allows you to access R via a web browser.
After you’ve analyzed the results, you may want to visualize them. Shiny is a great R package, licensed either commercially or under AGPLv3, that you can use to create interactive dashboards. Shiny provides a web application framework for R. It turns your analyses into interactive web applications; no HTML, CSS, or JavaScript knowledge is required. Shiny Server can deliver your R visualization to your customers via a web browser and execute R functions, including database queries, in the background.
RStudio is provided as a standard catalog item in Research Gateway for 1-Click deployment and use. AWS provides a number of tools like AWS Athena, AWG Glue, and others to connect to datasets for research analysis.
Benefits of using AWS for Clinical Informatics
Summary
Relevance Lab is a specialist AWS partner working closely in Health Informatics and Genomics solutions leveraging AWS existing solutions and complementing them with its Self-Service Cloud Portal solutions, automation, and governance best practices.
To know more about how we can help standardize, scale, and speed up Scientific Research in Cloud, feel free to contact us at marketing@relevancelab.com.
References
AWS Whitepaper on Genomics Data Transfer, Analytics and Machine Learning
Genomics Workflows on AWS
HPC on AWS Video – Running Genomics Workflows with Nextflow
Workflow Orchestration with Nextflow on AWS Cloud
Biotech Blueprint on AWS Cloud
Running R on AWS
Advanced Bioinformatics Workshop
AWS Cloud provides the right platform to scale Health Informatics and Genomic Research with security, data privacy, and cost-effectiveness. Relevance Lab offers a scalable architecture blueprint with Research Gateway and pre-built support for common researcher tools like BioInformatics starter kit, Nextflow, R-Studio, and Open Datasets to speed up Scientific Research on Cloud.
Click here for the full story.
Understanding the maturity model of automation with intelligent bots. Click here for the full story.
With growing interest & investments in new concepts like Automation and Artificial Intelligence, the common dilemma for enterprises is how to scale these for significant impacts to their relevant context. It is easy to do a small proof of concept but much harder to make broader impacts across the landscape of Hybrid Infrastructure, Applications and Service Delivery models. Even more complex is Organizational Change Management for underlying processes, culture and “Way of Working”. There is no “Silver bullet” or “cookie-cutter” approach that can give radical changes but it requires an investment in a roadmap of changes across People, Process and Technology.
Relevance Lab has been working closely with leading enterprises from different verticals of Digital Learning, Health Sciences & Financial Asset Management on creating a common “Open Platform” that helps bring Automation-First approach and a maturity model to incrementally make Automation more “Intelligent”.
Relevance Lab offers RLCatalyst – An AIOps platform driven by Intelligent Automation paves way for a faster and seamless Digital Transformation Journey. RLCatalyst Product is focused on driving “Intelligent” AUTOMATION.
Driving Automation to be more effective and efficient with “Intelligence” is the key goal and driven by a maturity model.
“Intelligence” based Maturity model for Automation
Level-1: Automation of tasks normally assisting users
Level-2: Integrated Automation focused on Process & Workflows replacing humans
Level-3: Automation leveraging existing Data & Context to drive decisions in more complex processes leveraging Analytics
Level-4: Autonomous & Cognitive techniques using Artificial Intelligence for Automation
AIOps Platforms need to have common building blocks for “OBSERVE – ENGAGE – ACT” functionality. As enterprises expand their Automation coverage across DevOps, TechOps, ServiceOps, SecurityOps, BusinessOps there is need for all three stages to Observe (with Sensors), Engage (Workflows), Act (Automation & Remediation).
RLCatalyst provides solutions for enterprises to create their version of an Open Architecture based AIOps Platform that can integrate with their existing landscape and provide a roadmap for maturity.
For more information feel free to contact marketing@relevancelab.com
Large enterprises with significant hybrid cloud workloads need a robust solution balancing self-service based consumption with proper governance and management.
Click here for the full story.
The adoption of Cloud and DevOps has brought changes in large enterprises around the traditional management methodology of Infra, Middleware, and Applications lifecycle. There is a continuous “tension” to achieve the right balance of “security + compliance” vs “agility + flexibility” between Operations and Development teams. For large enterprises with multiple business units and global operations and having distributed assets across multiple cloud providers, these issues are more complex. While there is no “silver bullet” that can solve all these issues, every enterprise needs a broad framework for achieving the right balance.
The broad framework is based on the following criteria:
The challenge being faced by larger enterprises using multiple cloud workloads is the lack of a common orchestration portal to enable application teams to have self-service requests and flexible workflows for managing workload configuration and application deployment lifecycle. The standard Cloud management portals from the major cloud providers have automated most of their internal provisioning processes, yet don’t provide customers system-specific solutions or do workload placement across various public and private clouds. In order to serve the needs of Application groups, a portal is needed with the following key functionalities.
Relevance Lab has come up with a microservices-based automation solution which automates enterprise multi-cloud provisioning, pre and post, provisioning workflows, workload management, mandatory policies, configurations, and security controls. The end-to-end provisioning is automated and made seamless to the user by integrating with ServiceNow, Domain servers, configuration servers and various cloud services. There are multiple microservices developed to handle each stage of the automation, making it highly flexible to extend to any cloud resources. The building blocks of the framework are as shown below:
The IAAC, which is maintained in a source code repository can have the cloud templates for a variety of resources.
| Resource | Platform | Automated Process |
|---|---|---|
| Compute – VM/Server | VMWare, AWS, Azure, GCP | Automated provisioning of VMs and the backup VMs |
| Compute – DB Server | VMWare, AWS, Azure, GCP | Automated provisioning of the DB servers and Backup servers – Oracle, PostgresSQL, MSSQL, MySQL, SAP |
| Compute – HA and DR | VMWare, AWS, Azure, GCP | Automated provisioning of HA and DR servers |
| Compute – Application Stack | AWS, Azure | Automated Provisioning of Application stack using CFTs and ARM templates |
| Network – VPC | AWS, Azure, GCP | Automated provisioning of VPCs and subnets |
| Storage | AWS, Azure, GCP | Automated provisioning of S3 buckets or Blob storage |
| Storage – Gateways | AWS | Automated provisioning of storage gateways |
| DNS Server | AWS, Azure | Automated provisioning of DNS servers |
Getting Started with Hybrid Cloud Automation – Our Recommendations:
Benefits:
For more details, please feel free to reach out to marketing@relevancelab.com