Best Ways to Support AI Workloads
Artificial intelligence promises tremendous benefits for organizations, but taking machine learning models into production presents challenges. Models require specialized infrastructure to support them. So with that said: what are the best practices for supporting AI workloads?
Understand Unique Infrastructure Needs
AI workloads have specific infrastructure requirements that differ from traditional applications. Key needs include:
- GPUs or other hardware acceleration for compute-intensive model training and inference
- Scalable storage and databases to handle large datasets
- High throughput networking to shuttle data between systems
- Orchestration software to deploy and manage containers and microservices
- Monitoring tools to track system performance and model reliability
Legacy infrastructure often lacks these capabilities. Building new infrastructure solely for AI can be complex and expensive.
Implement Flexible, Elastic Scaling
AI workloads are dynamic, with wild fluctuations in usage over time. Your infrastructure must auto-scale seamlessly to match demand. Over provisioning wastes resources when idle, while under provisioning results in bottlenecks.
Optimal solutions allow infinitely scaling compute resources from public cloud providers. Hybrid and multi-cloud approaches prevent vendor lock-in. Auto-scaling with scale-to-zero maximizes efficiency.
Prioritize Reliability and Availability
Downtime is disastrous for mission-critical AI applications. Ensure high service levels with redundancy across regions, automated failover, and other reliability best practices.
Monitor closely for any degradation in performance or accuracy. Track feedback loops all the way from data inputs to model outputs.
Secure Data and Systems
AI workloads involve sensitive datasets and algorithms. Secure systems against data breaches, model theft, adversarial attacks, and other threats.
Isolate workloads in containers with encrypted data pipelines. Follow cybersecurity best practices around access controls, network segmentation, and vulnerability management.
Minimize Operational Overhead
Complex infrastructure adds overhead for data scientists. Simplify tools and unify interfaces to remove friction from the inner loop of iterating on models.
Automate deployment, scaling, monitoring, and other tedious tasks through CI/CD pipelines and infrastructure-as-code scripts. Provide guardrails and policies to prevent misconfigurations.
Leverage AI-Optimized Platforms
Purpose-built AI platforms address many of these needs out-of-the-box. They insert seamlessly into existing workflows while abstracting away infrastructure burdens.
Leading solutions provide easy scaling, flexibility, reliability, security, cost optimization, ease of use, and integrations. They allow data scientists to focus on models while handling infrastructure.
Evaluate platforms based on your needs. For many, turnkey SaaS solutions simplify getting started with AI in production.
UbiOps and Bytesnet: supporting your AI workloads so you don’t have to
Companies like UbiOps and Bytesnet are leading the way in optimized AI infrastructure. UbiOps provides a serverless inference and training platform that abstracts infrastructure complexities so data scientists can focus on developing models. Bytesnet delivers high-performance computing capabilities tailored for AI workloads.
Together through a recent partnership, they now offer an integrated solution that combines UbiOps’s ease-of-use with Bytesnet’s scalable infrastructure. This showcases how purpose-built AI platforms and HPC providers can work together to make robust, reliable infrastructure accessible for organizations at any stage of their AI journey.
With the right platforms and partners, companies can support AI workloads seamlessly and achieve transformative business results powered by AI. The future is bright when infrastructure gets out of the way.
