Enterprise AI workloads need flexible, futuristic infrastructure capable of easy integration. Private and hybrid clouds are ideal solutions here. Read more.
Enterprise AI workloads have heavy requirements. From strategic planning to successful delivery, these needs are often unique in nature. The internet revolution changed the way humans live, and AI is one of the prime forces taking it forward. In today's world, enterprise-level AI is reshaping everything from businesses to customer service and interactions. Every major industry is either part of or feeling the ripples of this change.
It is imperative for IT teams within these enterprises to know that AI workloads need adaptable and futuristic infrastructure. Furthermore, they must also know that many existing technologies are ready and capable of easy integration with enterprise AI requirements. For example, cloud computing.
Cloud Computing and Enterprise AI Strategies
Not many IT resources are as easily manageable and available as the cloud. These significant benefits come as a boon to enterprise AI strategies, even at their prototype stages of AI development. Clouds can also help manage the large data sets involved in enterprise AI projects. Scalability is particularly beneficial here since the project demands are often dynamic and require more resources if needed, or less, to reduce wastage.
When talking about hosting enterprise AI infrastructure on the cloud, there are often talks of latency challenges as certain AI protocols require real-time responses and action. With the arrival of 5G and edge computing for faster processing and connectivity, latency issues become virtually nonexistent. Even otherwise, some of these challenges can be eliminated by using private and hybrid cloud models. Here's how.
Private and Hybrid Clouds to the Rescue!
Enterprises increasingly prefer private and hybrid cloud models to take care of their AI development strategies. Since real-time responses are critical here, on-premise private cloud models are preferred over hosted, using data center resources. Certain companies also go for a hybrid model involving both private and public cloud resources. Such an approach gives AI scientists the flexibility and efficiency they need to develop projects for enterprises successfully.
In the long-term, a private/hybrid cloud strategy leads to another significant advantage - cost savings. Since cloud providers are the ones responsible for any and all infrastructural upgrades, maintenance, updates, etc., combined with the fact that large instances allow for faster processing of enormous data sets, enterprises can save valuable time and resources and redirect their efforts into core AI development initiatives.
Cloud Adoption for Enterprise AI
With both AI and cloud computing witnessing rapid growth, enterprises can look forward to achieving their goals with ease. Experienced cloud providers can help enterprises with private or hybrid cloud adoption process. At VEXXHOST, our cloud services are based on OpenStack, making the environments free from licensing fees or vendor lockins. For many of our clients, private clouds are the preferred choice because of their highly scalable and secure nature.
Speaking of private clouds, you can now run on a fully agile and customized cloud from VEXXHOST, with no licensing fees and smooth 2-week migration. In fact, we're ready to put our money where our mouth is. We're so confident in being able to save you at least 20% or more on your current cloud infrastructure expenditure that if proven wrong- we'll give you $1,000 credit to our public cloud.
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