Understanding Cloud-based Generative AI

What is cloud generative AI?

Generative AI, also sometimes referred to as GenAI, is an artificial intelligence technology that enables users to create (or generate) novel content, such as unique images, animations, 3D renderings, audio, and text. GenAI is typically powered by AI models that are superior multitaskers with the learned “intelligence” to deliver outputs, or answers to user queries, in many forms—including question-and-answer content, summarization of large blocks of text, classification of data, and much more.

While generative AI models can be built and trained on on-premises infrastructure, more organizations and individuals are going to the cloud with their generative AI projects. All of the top cloud platforms, including AWS, Microsoft Azure, and Google Cloud Platform, offer a wealth of computational resources, datasets, and tools that can enhance and accelerate generative AI modeling, training, fine-tuning, and so on.

Cloud-based generative AI benefits from the same advantages the cloud brings to traditional computing, such as scalability, cost efficiency, flexibility, and ease in data analysis and sharing. It also gives organizations access to ultra-powerful GPUs and other high-performance infrastructure—that many organizations could never hope to afford for on-premises use—to train larger models for better and more relevant outputs.

How generative AI models work 

Generative AI works through the use of models, which analyze massive amounts of human-generated content extremely quickly to learn how to detect patterns and recognize specific objects.

There are three main types of generative AI models:

• Generative adversarial networks (GANs) – Ideal for image generation, augmenting data where information is scarce (such as in medical imaging), and creating high-quality images from textual descriptions.

• Variational autoencoders (VAEs) – Excellent for tasks such as anomaly detection and image reconstruction.

• Transformers – The model of choice for natural language processing tasks, including text summarization, conversational agents, language translation, and answering user queries.

Training generative AI models can be done through supervised or unsupervised learning. With supervised learning, the model is given millions of images that have been labeled to identify what they are—for instance, images of cats labeled “cat.” After processing these images, the model learns to identify and also generate images of cats in all their colors and patterns and various poses. This type of learning can lead to a model that is good at classification and regression tasks, such as predicting train arrival times or spam detection.

With unsupervised learning, the model processes large datasets that aren’t labeled and learns to discover patterns and relationships between various types of data. It is ideal for clustering and data analysis tasks, such as customer segmentation and anomaly detection.

Learning and model building in the cloud

Building and training generative AI models in the cloud can make the entire process faster and simpler. That’s because cloud platforms provide instant, on-demand access to the computing, memory, and storage resources organizations need right when they need them. Cloud providers also offer many advanced tools and services to streamline and enhance model development and training processes, from building to training to deployment. 

The nature—and flexibility—of cloud infrastructure enables developers to experiment easily and iterate quickly. For instance, they can switch up model architectures and hyperparameters without having to go through tedious and time-consuming setups. 

When it’s time to deploy, cloud providers’ tools typically make it straightforward to deploy and integrate into other applications.

Advantages of cloud-based generative AI for enterprises

Building and deploying complex generative AI models in the cloud comes with some distinct advantages: 

• Simplified scalability – It’s simple to scale resources up and down quickly in the cloud. That’s a plus for large GenAI models, which usually need a lot of compute power and memory. 

• High-performance computing – Cloud providers invest heavily in the latest, highest-performance GPUs and other hardware, which help accelerate training and inference processes. 

• On-demand storage – Just like the cloud makes it easy to spin up computing and memory resources as needed, it does the same with storage—which is critical when it comes to massive datasets and outputs common to generative AI models. 

• Ease of collaboration – With the cloud, sharing results and data among remotely separated colleagues is easier. When everything is in the cloud, teams can work in confidence that they’re working with the same data and maintaining consistency. 

• Reduced management burden – The cloud offers a variety of storage and management solutions, including data lakes and databases that can handle the enormous datasets that generative AI requires. 

Use cases for Generative AI in the cloud

• Fraud detection and proactive mitigation – GenAI can detect anomalies and suspicious activities in user accounts or online behavior and take automated action to mitigate impending threats or block them from further action.

• Predictive maintenance – Organizations can receive alerts when their expensive machinery and other assets are in danger of dysfunction or error. This can save a lot of time and money by addressing faults before they escalate.

• Chatbots and virtual assistants – Customer service departments can benefit from automated chatbots that take up some of the workload and can resolve minor issues without the need for human intervention.

• Automated resource management – Generative AI can help organizations get the most out of their cloud computing environments by optimizing and managing resource utilization. The models can even create synthetic data to add to existing datasets, which leads to better insights and more accurate predictions.

• Personalized user experiences and recommendations – GenAI is good at analyzing user patterns and making relevant recommendations or personalizing the online customer experience to each individual’s preferences.

• Creation of virtual worlds and environments – Some models can build virtual environments according to user specifications in real time. This can be helpful not only in the gaming industry, but also in product design and testing—for instance, building a virtual 3D world in which to test an autonomous vehicle.

How Nutanix facilitates generative AI

As a leader in hybrid multicloud solutions, Nutanix is committed to delivering the advanced solutions our customers need. AI technology is evolving at breathtaking speed, and as organizations go to the cloud to get the best AI capabilities, Nutanix is here to make it simple and stress-free. Nutanix Enterprise AI simplifies enterprise generative AI (GenAI) operations and management with an easy way to deploy your choice of LLMs and secure endpoints for faster GenAI that's resilient and includes day 2 operations out of the box. That means running even the most complex GenAI projects will be smooth and seamless in whatever environment or cloud it’s in.

For organizations that might not be able to take advantage of cloud-based generative AI training due to data privacy or sovereignty laws, Nutanix provides Nutanix GPT-in-a-Box, a Nutanix validated stack of AI infrastructure and services that allows organizations to start their generative AI journey in edge or on-premises data centers for complete control over sensitive data.