Cloud-native is the architecture of choice to build and deploy AI-embedded CI applications because it offers benefits to both the business and developers.
Continuous intelligence (CI) relies on the real-time analysis of streaming data to produce actionable insights in milliseconds to seconds. Such capabilities have applications throughout a business. In today’s dynamic marketplace, new CI applications that use data from various sources at any given time might be needed on very short notice.
The challenge is how to have the flexibility to rapidly develop and deploy new CI applications to meet fast-changing business requirements. A common approach employed today is to use a dynamic architecture that delivers access to data, processing power, and analytics capabilities on demand. In the future, solutions also will likely incorporate artificial intelligence applications to complement the benefits of traditional analytics.
See also: The Case for Continuous Intelligence
Increasingly, cloud-native is the architecture of choice to build and deploy AI-embedded CI applications. A cloud-native approach offers benefits to both the business and developers. Cloud-native applications or services are loosely coupled with explicitly described dependencies. As a result:
- Applications and processes run in software containers as isolated units.
- Operations are managed by central orchestration processes to improve resource usage and reduce maintenance costs.
- Businesses get a highly dynamic system that is composed of independent processes that work together to provide business value.
Fundamentally, a cloud-native architecture makes use of microservices and containers that leverage cloud-based platforms as the preferred deployment infrastructure.
Microservices provide the loosely coupled application architecture, which enables deployment in highly distributed patterns. Additionally, microservices support a growing ecosystem of solutions that can complement or extend a cloud platform. A cloud-native approach to CI applications uses containers to provide the underlying infrastructure and tools to make use of a microservices architecture.
Containers are important because developing, deploying, and maintaining CI applications requires a lot of ongoing work. Containers offer a way for processes and applications to be bundled and run. They are portable and easy to scale. They can be used throughout an application’s lifecycle from development to test to production. They also allow large applications to be broken into smaller components and presented to other applications as microservices.
Another aspect of a cloud-native deployment is the use of serverless computing. Serverless computing is a cloud-computing execution model in which the cloud provider runs the server and dynamically manages the allocation of machine resources. From a CI perspective, serverless is an event-driven environment in which containers are loaded and executed based on some condition being triggered. That condition might be an API call or the time of day.
Meeting the Demands of CI Applications
At the heart of any CI effort is the streaming and historical data to be analyzed. Managing the data, preparing the data for analysis, ensuring access to the data, and safeguarding the data can be formidable tasks. The right architecture can simplify and unify how businesses collect, organize, and analyze data to accelerate the value of real-time analytics and predictive AI. The growing use of cloud-native architectures makes it easier for businesses to develop, deploy, and run CI across the enterprise.
Specifically, a cloud-native architecture enables CI by:
- Making it easier to access data regardless of where it is located or what kind of data is used
- Ingesting streaming data and analyzing data on the fly
- Allowing the use of the most suitable AI or analytics processes for the given business need
- Hosting data and running analytics on different platforms
- Flexibly scaling resources up or down to meet changing demands
Teaming with a Cloud-Native Technology Partner
IBM has been a leader in aiding the industry move to cloud-native applications. In the past few years, it has transformed its software portfolio to be cloud-native and optimized it to run on Red Hat OpenShift. With this transformation, businesses can build mission-critical CI applications once and run them on all leading public clouds—including Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, Alibaba, IBM Cloud—as well as on private clouds.
With specific regard to CI, IBM addresses streaming data ingestion and analysis issues with IBM Cloud Pak for Data. IBM Cloud Pak for Data is a fully integrated data and AI platform that helps businesses collect, organize, and analyze data and infuse AI throughout their organizations. Built on Red Hat OpenShift Container Platform, IBM Cloud Pak for Data integrates IBM Watson AI technology with IBM Hybrid Data Management Platform, DataOps, data governance, streaming analytics, and business analytics technologies. Together, these capabilities provide the architecture for CI that can meet ever-changing business needs.
IBM Cloud Pak for Data is easily extendable using a growing array of IBM and third-party services. It runs across any cloud, allowing businesses to integrate their analytics and applications to speed innovation.
Complementing IBM Cloud Pak for Data, IBM Cloud Pak for Data System is a cloud-native data and AI platform in a box that provides a pre-configured, governed, and secure environment to collect, organize, and analyze data. Built on the same Red Hat OpenShift Container Platform, IBM Cloud Pak for Data System gives businesses access to a broad set of data and AI services and allows quick integration of these capabilities into applications to accelerate innovation. The hyperconverged, plug-and-play system is easily deployable in 4 hours.
To learn more about how a cloud-native architecture can help with your CI efforts, visit Cloud Pak for Data.