Although enterprises have great potential with AI, it is hidden like a beast in the cave. If enterprises can see it through then they can do many things which they can't do right now. The key to do this is bringing AI and analytics into the production line. Here are some possible ways to make this easy and simple:
Multi-tenancy is an essential piece of today’s enterprise environment and critical for the success of artificial intelligence. Multi-instance software and computing platform simplify load utilization and resource utilization. Enterprise architectures using multi-tenancy can insulate independent tenants, minimize the impact of outages, and enable shared access to enterprise data. The data can be exposed easily with everyone commingled on a centralized database. Large-scale analytics and Big Data systems whether in development or production can be easily supported in this ecosystem.
Flexibility helps in counteracting roadblocks to high-performance computing. Cumbersome systems cannot be scaled for thousands of models and hyperparameters and petabytes datasets. It can be infeasible to make design changes for thousands of models and algorithms that are becoming the norm. Therefore, enterprises should be able to architect and deploy flexible Big Data solutions to support huge data-intensive applications in production.
Data Fabric is a service platform that enables enterprises to manage multi-dimensional and large-scale data in simple ways. Business and IT teams can control and manage data for a wide range of applications and diverse purposes. Modern-day data sciences and edge level analytics need this ability for smoothly delivering data to wide sources and variety of applications including data centers, multi-cloud applications, etc. Enterprises don’t have to rely on crude methods for data transformations and data moment that are costly and cumbersome operate.
Smoother data preparation is the key to successful production. To simplify data preparation, enterprises should consider data streaming capability for collecting, cleaning, and consolidating data from unstructured and messy sources. It helps in creating workflows for preparing data and preventing errors from entering the production. Apart from this, teams get the ability to clean from original dirty data sources.
A hybrid integration platform is required to harness the power of AI and analytics. Teams can leverage this platform to extend applications anywhere across multiple data centers in cloud or on-premise. Teams can get unique abilities to mirror data and table replication. The same platform can be extended across multiple centers and geo-distributed locations.