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10 benefits of containers for AI workloads
IT organizations face a daunting onslaught of new AI projects. Containers offer key benefits to ease the burden throughout an AI workload's lifecycle.
Containers can be a great equalizer and familiar foundation for DevOps teams embarking on their first AI development projects.
If your organization already has container expertise, it will serve first-time AI initiatives well to extend those DevOps and container strategies and tooling to AI.
If your organization is newer to containers, start with a small pilot project where teams can refactor development practices to include containers and AI workloads simultaneously. The interconnection of so many services that are new to the tech stack could lead to a pile-on of complexity if left unmanaged. Teams might need to add skills in microservices architecture and container development and management. Experiment, build a proof of concept and then scale AI development up to meet the business's ambitions.
Learn why organizations are turning to containers to host AI workloads, and the 10 benefits to expect from a container-based project.
Containerization shapes the future of AI development
Initial AI projects demand rapid iteration and development. Containers empower teams to experiment with different AI models, libraries and tools without the need to modify the infrastructure.
Container scalability enables developers to start small with AI development projects. Once the concept is approved, containers can underpin a full production deployment without a major overhaul. AI development demands high-performance computing resources, scalability and orchestration. Expect containers to support a wide range of current and emerging AI frameworks.
10 benefits of containers
The benefits of containerization for AI development include dependency management, environment consistency, scaling, resource consumption efficiency, isolation, reproducibility, security, latency control, versioning and cost management.
1. Dependency management
AI applications often require specific versions of libraries and frameworks, such as TensorFlow or PyTorch. Containers include all the necessary dependencies within their environment, which prevents issues related to differing versions or configurations on host machines.
2. Consistent environment
Containers ensure that your AI application runs the same way, regardless of where it's deployed. This benefit eliminates issues related to discrepancies between development laptops, the testing sandbox and production environments, even across hybrid and multi-cloud setups.
3. Scalability
Orchestration systems like Kubernetes manage and scale containers up and down with workload demands. This scalability is essential in AI development to handle variable workloads and efficiently manage resources. Examples include training large models or deploying multiple instances for inference, which is how AI models make predictions and conclusions about data.
4. Resource efficiency
AI workloads can be resource intensive. Containers require fewer resources than VMs because they share the host system's OS kernel rather than requiring one OS per instance. More lightweight containers can run on the same hardware than comparable VMs.
Containers can also reduce the risk of cloud service provider lock-in for organizations that want to move and scale AI workloads in multi-cloud and hybrid environments.
5. Isolation
Containers provide isolation from the host system and other containers on the shared OS. This isolation can enhance security by limiting the impact of malicious or malfunctioning applications.
As already described, containers isolate dependencies for each project. Different AI projects can use different versions of libraries like TensorFlow or PyTorch on the same physical machine without interference.
6. Security
Isolation enhances security by limiting what each container can access. AI workloads require a lot of training data. Sometimes, sensitive data is input into an AI application. A container running an AI model that processes sensitive data can be restricted in its access to the host system or other networks.
If your organization is still maturing its container security, focus on best practices before moving into AI development.
7. Latency with separate services
Containers support microservices architecture. Different components of an AI application can run in separate containers. This setup enables independent scaling and management for each service. Done correctly, a microservices deployment on containers will optimize performance and reduce latency.
8. Version control
Every aspect of the environment can be version controlled in a container. This helps teams to precisely track experiments. They can identify which changes to the environment or codebase led to changes in model performance.
9. Reproducibility
Developers can use containers to capture a snapshot of a state. This enables them to revert to a specific point in the AI development process. It helps to troubleshoot and debug projects.
10. Reduced staff costs
Containers can reduce the labor cost associated with an AI build. Going with containers for AI development reduces the time developers spend on environment configuration and troubleshooting. Rapid deployment capabilities also mean that DevOps teams can push updates and improvements quickly.
Containers integrate well with CI/CD pipelines, facilitating automated testing and deployment. With less manual intervention from development to deployment, human errors should be decreased. These aspects reduce potential expenses related to bug fixes and downtime.
Will Kelly is a freelance writer and content strategist who has written about cloud, DevOps, AI and enterprise mobility.
