Why Do We Need AI Infrastructure? 

In a blog focused on AI data centers, we expressed the large power requirements of artificial intelligence (AI), so much so that organizations are scrambling to build facilities solely dedicated to powering AI. Every aspect of AI and machine learning (ML). from training and running AI models, to operating and hosting, requires immense power and cooling. In order to meet these energy requirements, organizations have created custom hardware and software solutions, as well as practices, to support AI in its journey, culminating in the creation of AI infrastructure.

But what is AI infrastructure? 

AI infrastructure refers to the environment of hardware and software created and compiled to support the creation and operation of AI/ML workloads. The primary function of this type of infrastructure is to facilitate the processing and analysis of data within the AI scope, and knowing AI requirements, the amount of data needed will be seemingly endless. What sets AI infrastructure apart from any other solution infrastructure are its intense resource needs, requiring things like:

• GPUs and TPUs
• Specific AI hardware
• Low latency tech
• AI-centric software and frameworks

And much more.

Simply put, AI infrastructure is the tech and hardware stack of AI. However, upon closer look at AI infrastructure, there is much more happening behind the scenes.

AI Infrastructure Requirements

Every aspect of AI infrastructure is thoughtfully designed to facilitate smoother AI processes and faster data processing. From tech responsible for careful data handling, to the hardware crafted to house such monoliths, to even the cooling meant to keep energy emissions at bay, AI infrastructure has its hands full. With that in mind, infrastructure provisioners have split AI systems into 3 different layers, each representing requirements of successful AI/ML operations:

• Applications Layer: the layer that provides end users with AI applications and solutions
• Model Layer: the layer that facilitates the creations and training of ML and deep learning models that power said applications and platforms
• Infrastructure Layer: the layer of hardware and software that supports model creation and training and application operations

These layers encompass the main offerings of AI infrastructure: hardware and software.

AI Infrastructure Hardware

The hardware within AI infrastructure varies greatly from that of traditional infrastructure.

Where a traditional data center would house central processing units (CPUs) to execute tasks, AI infrastructure and data centers rely on hardware such as:

• Graphic Processing Units (GPUs): Electronic circuits originally designed to accelerate image rendering, but in the context of AI, have been shifted towards training and running AI models parallel to one another
• Tensor Processing Units (TPUs): Processors designed to speed up tensor computations- data structures used within deep learning models- allowing for low latency computations with high output
• AI Accelerators: Hardware- including, GPUs, neural processing units (NPUs), custom chips, and more- designed to increase AI/ML model and application performance and consequently reducing training and inference time

In the larger sense of infrastructure, liquid cooling is another crucial component of AI hardware, as it provides competent heat transfer to facilitate stronger data processing.

AI Infrastructure Software

While the hardware required by AI is straightforward, the software these solutions need is more nuanced, using different data and machine learning solutions to facilitate processing. Data software components critical to AI infrastructure include:

• Data Storage: AI should be trained on large volumes of data to produce better output, large and scalable data storage (ex: data warehouses, data lakes, cloud data storage) are absolutely essential in AI infrastructure
• Data Processing Libraries: Before data is ingested by AI models, data processing libraries prep by cleaning, transforming, and structuring data, enabling faster data distribution and use
• Data Management: What good are processed and stored data if not managed properly; competent data management software allows users to use data correctly, streamlining analytics extraction as well as compliance processes

Software requirements don’t just stop with data tools. Specific machine learning software used within AI infrastructure include ML frameworks and MLOps platforms.

Machine learning frameworks are instrumental to the success of machine learning models, providing every necessity needed to design, train and deploy ML models. These specific frameworks support AI applications in various ways, including speeding up GPU tasks critical to ML training, optimizing processing, and offering tools important to AI development. Two great examples of ML frameworks are TensorFlow and PyTorch!

Machine learning operations, or MLOps, are practices and tools that streamline machine learning lifecycles. MLOps does so by automating and managing important ML processes, including data collection, model training, and monitoring. MLOps platforms are therefore the platforms that oversee entire MLOps practices, facilitating all the necessary automations as well as creating deployment pipelines and tracking performance.

AI infrastructure requirements may seem far and wide (certainly more demanding than traditional infrastructure), however, the benefits created support AI solutions like no other.

The Benefits of AI Infrastructure

Powered by high performance computing (HPC) tech, complex data tools, and futuristic ML algorithms, AI infrastructure and the solutions it supports are ushering in the future of tech. There are good reasons why some of the most important AI/ML developments and solutions depend on these highly specialized hardware and software stacks. Some of the best reasons for AI to be hosted within AI infrastructure include:

Unparalleled Scalability and Flexibility 

The datasets that AI models are trained on are seemingly endless, growing more and more as models evolve over time. Questions about an infrastructure’s scalability are certainly valid, though the typical AI infrastructure is more than well-equipped to handle large and complex datasets.

AI infrastructure is largely cloud-based, allowing greater scale and flexibility than on-premise hosting. This cloud foundation enables infrastructure to scale intuitively and within the reins of the user’s resources, offering optimized scale that won’t  drain resources. Scalable infrastructure is also a testament to a system’s overall flexibility, with AI infrastructure often offering flexibility in the form of workload-based autoscaling.

Reduced Costs

In the same vein of scalability and resource optimization, AI infrastructure ensures that resources are used efficiently. Despite this, it’s undeniable that the components that make up this type of infrastructure are costly, though not as costly and as time-consuming as if trying to develop the same AI solution within traditional infrastructure.

Specialized components built for AI will cost more than traditional infrastructure components initially, however, costs are reduced dramatically as development progresses. The ROI that AI infrastructure will yield will make more sense financially than trying to make an AI solution work within the confines of traditional infrastructure.

Increased Speed and Performance

AI infrastructure uses custom hardware dedicated to strengthening AI solutions and training. These HPC technologies perform a variety of tasks, including parallel processing capabilities and powering ML algorithms, while staying smaller than traditional chips and offering low latency capabilities. All of the offerings from these HPC technologies are geared towards dramatically reducing ML processing time and bolstering model performance, two aspects that are critical in an industry of fast growth like AI.

Stronger Decision Making

Faster and more optimized model training, strong data software, and streamlined solution deployment offer tons of benefits of business-side analytics. With AI infrastructure being more than capable of handling larger and more complex data sets, the insights extracted using AI solutions will offer more refined outputs in real time, leading to faster and more informed business decisions.

Enhanced Reliability and Uptime

AI infrastructure is designed to provide models and solutions with uninterrupted access to data sets and compute power. Through system redundancy, backups, and more, data scientists and AI users alike can increase reliability, with AI infra's ability to scale dynamically improving uptime.

Organization-Wide Collaboration

MLOps platforms not only provide a management interface for AI solutions, but also create a unified platform for developers and engineers alike. Through MLOps, parties relevant to AI solutions and infrastructure will have shared systems and processes needed to develop more efficiently. 

Approaching AI Infrastructure with Lyrid

Artificial intelligence and machine learning development shows no signs of slowing down, with groundbreaking innovations and progressive steps towards their evolution being made seemingly every single day. With the amount of AI/ML solutions flooding the market, it begs the question: “what powers this tech?”.

AI infrastructure represents the cumulative hardware and software that is specially designed to handle AI workloads. From data gathering and processing, to model training and refinement, AI infrastructure handles AI related processes through various HPCs, data software, and ML frameworks and platforms. With benefits such as increased scalability and speed and reduced costs, building your own AI infrastructure is the logical next step in advancing your AI development.

But it’s easier said than done.

AI infrastructure requires careful hardware and software configuration, with competent data tools and complex ML frameworks adding fuel to the fire. Or … you could host your AI systems within Lyrid!

Lyrid offers a combination of cloud infrastructure mobilized through a network of localized data centers across the globe! Deploy confidently across the edge, on-premise, or in different cloud environments, with features like:

• All-in-one monitoring and observability
• Version updating and rollback
• Automated scaling
• Regulation compliance

And more making your deployment and hosting experience a breeze! 

Host the next big thing on Lyrid.io, and book a meeting with one of our product specialists for a free demo!