Artificial intelligence (AI) is transforming nearly every industry. However, implementing AI in your organization is not as simple as flipping a switch. One of the biggest roadblocks is infrastructure. In this post, we’ll explore why infrastructure is a major challenge for AI adoption and how to overcome it.
The Problem: Legacy Infrastructure
AI workloads are not your typical IT processes. Training a custom model or running large-scale inference operations requires a level of compute power, storage, and scalability that many traditional enterprise systems simply weren’t designed to handle. Additionally, enterprises in highly regulated industries cannot send data to public large language models like ChatGPT over the internet, not to mention the sheer amount of data that would need to be transmitted outside their network.
As a result, these organizations must train, test, and deploy models on their internal networks and infrastructure to ensure compliance and data security.
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Legacy Infrastructure Challenges
Below are the three most common challenges we face when helping small, mid, and large enterprise organizations adopt AI:
- Compute Power: Most traditional enterprises, whether on-premises or in the cloud, still use traditional CPU and memory-intensive machines. Although powerful, these machines are not adequate for training models using techniques like deep learning. Graphical Processing Units (GPUs) are essential for training your models but are costly, making upgrades a financial challenge.
- Data Storage and Transfer: Training AI models requires massive, often unstructured datasets. Legacy infrastructure often can’t keep up with the amount of data being transferred, leading to bottlenecks.
- Scalability: AI workloads fluctuate in both training and running them. Most enterprises use fixed-capacity systems that can’t scale dynamically. This inflexibility leads to inefficiencies and can greatly affect costs.
The Solution: Cloud Infrastructure
When we begin working with a client who is starting to adopt AI into their workflows, we start by auditing their existing infrastructure. We often find that their infrastructure is outdated, sometimes from a decade ago, and not suitable for training or deploying models.
Despite reservations from stakeholders, in most cases, we recommend transitioning to modern, cloud-based infrastructure. Platforms like Azure, GCP, and AWS provide:
- Machines with powerful GPUs.
- Elastic storage for data
- Auto-scaling capabilities.
- Flexible pay-as-you-go model, making them cost-effective and flexible.
Conclusion
Modernizing infrastructure through the adoption of advanced cloud solutions allows enterprises to effectively address these challenges and fully leverage the potential of AI. However, it is crucial to approach infrastructure modernization with caution. We recommend beginning with an AI strategy that thoroughly examines your infrastructure, data, personnel, and processes. LefeWare Solutions can assist you in this endeavor.
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