Productivity – AI-based algorithms required significant admin time and effort in terms of building testing and production environments, software management, and provisioning hardware resources for compute operations and storage. A centrally managed hybrid cloud or a public cloud does away with this, leaving IT staff to focus on non-repetitive tasks.
Automation – AI is also being embedded right into the cloud infrastructure to help automate routine processes and streamline workloads. In a hybrid cloud environment, AI tools can be used to monitor, manage, and self-heal individual public and private cloud components.
Analytics – Data residing in most cloud workloads needs to be analyzed for more insights. AI-based models make it easy to mine this data in real time and develop native analytics and dashboards for each of these applications.
Data management – AI helps boost cloud workloads in customer service, marketing, ERP, and supply chain management by processing and generating data in real time. For example, AI tools embedded in Dataflow, the streaming analytics platform in Google Cloud, can enable functions as varied as programmatic bidding in media advertising, fraud prevention in financial services, threat detection in IT security, and personalized shopping recommendations in retail.
Better SaaS tools – Perhaps the most obvious and popular use of AI-based algorithms is their integration in mainstream SaaS tools to help these deliver more functionality and value to end users. For example, Salesforce added “Einstein,” an AI-based algorithm to its flagship CRM system to help customers make sense of the immense volumes of data generated, find patterns in this data, and derive actionable insights to improve their sales strategies.
This is but one example in a landscape of literally hundreds of AI-enabled SaaS tools.
Challenges in Deploying AI in Cloud Environments
Merging AI and the cloud isn’t always cakes and ale. The main concerns are data privacy and connectivity.
Data privacy – The pay-as-you-go model of SaaS technology allows thousands of companies across the world to make sense of data, find efficiencies in routine processes, develop new products, and even expand into new verticals. Therefore, they run their customer, vendor, and market data through cloud applications with little-to-no appreciation of the security risks of the public cloud. When AI processes data fed into a SaaS tool in a public cloud environment, it amplifies these risks on an exponential scale. Sensitive company data could be exposed to a security breach or unauthorized access when the processes and perimeters for AI algorithms are not clearly defined.
Connectivity – Any algorithm or data processing system in the cloud depends on one thing to keep it running: a steady internet connection. Poor network connectivity can slow down ML processes and defeat the purpose where real-time transactions and analytics are involved.
Hybrid Cloud – The New Home of AI
The enterprise is looking to AI for more and more real-time insights that drive innovation and give it a competitive advantage. For this, it needs a robust infrastructure that can handle vast amounts of data while guaranteeing security and functionality for end users.
Nutanix has partnered with NVIDIA and Mellanox Technologies to create an AI-ready hybrid cloud infrastructure that enables companies in retail, healthcare, finance, aerospace, and other industries to develop turnkey AI-based solutions and applications. Indeed, AI has finally found its castle in the cloud!