Artificial Intelligence or AI is ubiquitous these days. No organization can escape its influence. So instead of avoiding change, business leaders and IT decision-makers must embrace it.
Adopting AI into your practice is easier said than done. It’s a vast domain that encompasses machine learning, deep learning, neural network, among others.
So, there’s a lot of planning and a certain amount of investment involved. And part of it goes to setting up an infrastructure where you can run AI tools.
But what does this infrastructure look like, and what are the requirements of such an infrastructure? This article explains the infrastructure requirements for AI.
What is an Infrastructure?
It’s important to understand what infrastructure means in terms of AI. Infrastructure refers to all the components necessary for cloud computing. Therefore, the more precise term to use is “Cloud Infrastructure.”
The components include hardware, storage, network resources, and software applications. You rent these from a company that manages servers and specializes in cloud technology.
Cloud service providers often sell “Infrastructure-as-a-Service” (IAAS) to companies that want to use AI.
You can also set up your own infrastructure from scratch. Below are some of the requirements for setting up one:
- High Computing Power
- Memory and Storage
- Network Infrastructure
- Non-hardware Related Considerations
Let’s explain each requirement in greater detail.
High Computing Power
The power of AI doesn’t lie in storing, labeling, and categorizing data. It lies in the ability to process a vast amount of data to make sense out of it. And for that to happen, algorithms need high computing power. So, the first requirement you need to meet is to arrange resources for computing power — which is the servers’ hardware.
The hardware consists of processors, motherboards, RAM, cooling fans, among other things. In terms of computing power, you need to zero in on the processors. That’s because they largely determine how fast your AI will be.
The two most common processors are CPU and GPU. CPUs are the most common processor since we have them on our laptops and computers. But even the best CPUs are incapable of handling complex calculations. They are reserved for the basic workload. The best processors for AI are GPUs or Graphics Processing Units. They can accelerate your AI processes by as much as 100X.
You should look for processors with the label “AI accelerator” if your project demand capabilities like machine learning, neural networks, machine vision, robotics, and natural language processing.
Memory and Storage
The next requirement for AI infrastructure is that of memory and storage. AI processes need a lot of and different types of data. Depending on your project, you’d be using real-time data or historical data, or both. But first, you have to get storage space to store them for later use.
Data storage requirements for AI vary greatly as per the application and scope.
A company like Facebook, where 350 million photos are uploaded per day, it’d need massive data centers to store and analyze them. But if the task is as small as processing a 10-minute audio clip per day, even a regular PC is more than enough.
So the first job is to determine the scope of your project and then estimate how much AI data applications will generate. The more data you have to deal with, the more storage space you should get.
Memory is another consideration. AI applications need specific types of memory, which are on-chip memory, high bandwidth memory (HBM), and GDDR memory.
On-chip memory is built within the chip and offers the best power efficiency and latency. HBM and GDDR are external memories with better capacity.
AI requires multiple applications working simultaneously with one another to produce desired results. For example, you might have stored the data in one container. And then, the ML algorithm will be in another container. These two containers need to communicate with one another to work. To facilitate that communication, you need to set up the network infrastructure.
Deep learning algorithms, especially, are highly dependent on communications. As the algorithms grow in size and number, the network supporting that must keep pace. Therefore, you should select a network with scalability in mind.
Ideally, the network infrastructure should offer high bandwidth and low latency. In specific processes like computer vision, the network should support real-time data transmission.
Select a service provider that can ensure service wrap and consistent technology stack across all regions.
The cloud infrastructure that hosts your AI applications and projects should be secure. So security is vital when building the infrastructure.
As already mentioned, AI processes require a vast amount of data. You need to store them on the cloud or a local server so that the algorithms can easily access them. But the endpoint will remain open to anyone with the correct credentials. If hackers get hold of those credentials with malpractice, they may compromise the integrity of the data and hence the result. This will make your AI processes less accurate and efficient.
Also, you need to secure your AI and ML models that are trained with data. It’s best to adopt an end-to-end security solution. Encrypt the data, adopt IAM, implement firewalls, limit the use of VPN, avoid public internet connections to make the infrastructure secure.
Non-hardware Related Considerations
There are other things besides the traditional infrastructure requirements to run AI processes effectively.
The first is to define policies across the company on how to handle data. This should include data governance rules that should be applicable to all employees and software. The laws also protect data from falling into the wrong hands.
The second is to establish data scrubbing practices. This ensures data is consistent with your goals. By feeding your algorithms the best data, you can expect the best results.
You will also need an MLOps platform like Valohai, to manage automated deployment, configuration, monitoring, resource management, testing, and debugging.
Lastly, you should think about people. You can have the best hardware, but your projects will most likely fail if your team isn’t prepared or properly trained to handle it. So, conduct proper training programs for your employees who are supposed to work on the projects.
AI Infrastructure is complex, but you don’t have to take care of everything yourself. Service providers like RoseHosting Cloud offer best-in-class GPUs, security, and hosting solutions to anyone who needs to adopt AI. So you can get started fairly quickly without much investment.