As organizations race to adopt generative AI, IT teams are encountering significant hurdles. AI requires high-performance, highly reliable and scalable infrastructure, and many IT teams are facing the need for major upgrades to support gen AI applications and workloads. They also need an efficient way to move AI from development in the public cloud to deployment in the private data center.
That’s why containers are becoming the de facto standard for AI workloads. In a recent survey of 1,500 IT and platform engineering professionals conducted by Vanson Bourne, 70 percent of respondents said they plan to containerize gen AI applications. Gartner predicts that more than 75 percent of gen AI deployments will use containers by 2027, up from 50 percent today.
Containers enable developers to package an app and all of its required runtime components in a small, portable bundle that can easily be moved among various machines and systems without modifying any code. This “build once, use anywhere” model provides the application portability that is essential for organizations running multi-cloud and hybrid environments.
Containers also offer a number of advantages that make them ideal for AI development. Here are five key benefits.
Resource efficiency. AI workloads are resource intensive and can be highly variable. Containers use fewer resources than virtual machines (VMs) because they share the same OS instead of requiring their own OS instances. They also manage and optimize the use of compute resources. Additionally, various AI components can run in separate containers in a microservices architecture. Each container can be scaled independently to optimize performance.
Workload isolation. AI development typically involves multiple iterations using a variety of tools and libraries to refine the model based on its performance during training. That’s a marked departure from the traditional “waterfall” approach to producing monolithic applications with tightly integrated components, code modules and services. Because containers isolate the workload from the host system, they prevent conflicts between dependencies. Developers can use different libraries on the same physical machine.
Consistency. Because of the iterative nature of AI development, organizations need the ability to test new models and algorithms without being tied to a specific infrastructure. Containers allow AI applications to be packaged uniformly so that they run the same way in development,
testing and production. They also enable the deployment of apps across multiple cloud, on-premises and hybrid environments.
Security. A key challenge with AI development is protecting sensitive data used in training and inference. Containers make it possible to restrict access to AI models that process sensitive information. Role-based access control provides granular control over who can perform what actions within a containerized system. This helps enhance security and simplify access management, which is particularly important in highly regulated industries such as finance and healthcare.
Automation. Orchestration tools automate the management of containers, including tasks such as deployment, networking and scaling. Container orchestration reduces deployment time, optimizes resource allocation and improves scalability. It also simplifies management and automates many administrative tasks, reducing infrastructure costs and operational overhead by minimizing the time developers spend configuring the environment. Less manual intervention means fewer human errors, reducing the costs of troubleshooting and downtime.
Technologent has a proven track record of success in helping clients take advantage of containerization. We also have a team of engineers who can help you leverage the right tools, practices and integration models to rapidly develop and deploy AI applications and ecosystems.
If you’re looking to leverage containers for AI development and deployment, give us a call. We can help you map out the right strategy to ensure the success of your AI initiatives.