Developing an artificial intelligence application or tool is by no means a simple task, but one that all sorts of businesses are diving into. It seems that everyone has identified a business action that can be improved by providing some sort of AI integration.
Defining the need is one thing, but developing the process is the sticky part. Laying aside code development and model training (and that is a big set of tasks to lay aside), IT staff can get headaches simply over gathering and maintaining the reams of data required.
But if AI is going to be useful, it needs to learn quickly and it needs to be accurate. That requires a database application that is versatile, robust, and able to handle complex queries effectively.
MongoDB is a database platform that is purpose-built for an AI development environment. Its document-store model allows diverse types of data to be stored together, simplifying the AI model querying path. There is no need to access different types of databases for queries depending on the data type. Its scalable clustering technology grows as the dataset enlarges. MongoDBโs hybrid-cloud deployment model featuring its cloud-based Atlas platform means that the dataset data can be positioned where it needs to beโon-premise or in the cloud.
Another task that frequently influences AI environment creation is, frankly, cost efficiency. Much of the AI hardware stack must be devoted to ultra-performance for modeling and deployment. How can IT departments reduce the cost allocation for their AI projects?
One answer could be deploying hypervisor technology. For example, the database clusters that house the modeling datasets could be deployed on virtual machines, maximizing the usage of the server hardware. Since this portion of the environment does not necessarily need dedicated GPUs to do its job, this might be an opportunity to maximize compute efficiencies.
The HPE VM Essentials Software virtualization platform could be a compelling solution for MongoDB administrators looking to drive efficiencies in their compute hardware. HPE VM Essentials can also provide a seamless transition for VMware users looking for an alternative hypervisor solution. It can manage both existing VMware virtual machines as well as its own. It offers an organizational and deployment structure that can simplify MongoDB sharded cluster management by dividing shards between HPE VM Essentials instances. It also provides an upgrade path to the HPE Morpheus Software enterprise-class hybrid-cloud management solution that can manage hypervisors, public cloud environments and Kubernetes deployments all from one interface. The result is a comprehensive AI development environment solution that fits perfectly with the MongoDB hybrid-cloud message.
Before exploring these higher-level solution concepts, however, it is best to discuss the advantages of simply deploying a MongoDB replica set or sharded cluster in an HPE VM Essentials environment. HPE Hybrid Cloud Solutions Engineering recently released a technical brief and accompanying video that explains why MongoDB administrators can benefit from deploying their virtualized clusters using HPE VM Essentials. Both go into more detail about the solution, and the video even includes a short demo showing the simplicity of generating a MongoDB replica set on the HPE VM Essentials KVM-based hypervisor.
After reading the brief and watching the video, try the solution out for yourself by downloading a trial version of the latest HPE VM Essentials release. Follow the step-by-step activation guide training on the page, then click "Start with a free trial" to download and spin up an HPE VM Essentials setup for testing.
From there, use the video demo as a guide to see how quickly and efficiently a MongoDB cluster can be used to start maximizing your AI development investment.
Learn more
Technical brief: Deploying a virtualized MongoDB cluster on HPE VM Essential
Video: HPE VM Essentials โ Deploying virtual MongoDB clusters
Meet HPE Blogger Kevin McNally, Senior Systems Engineer, HPE Hybrid Cloud Solutions Engineering
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