The Fifth Elephant 2025 Annual Conference CfP

Description

This session will explore the practical challenges and solutions for deploying image generation services at scale in production environments. As AI-powered image generation becomes increasingly mainstream, organizations face critical decisions about infrastructure deployment, cost optimization, and service reliability. The talk will cover real-world experiences with different deployment strategies, from managed APIs to self-hosted solutions, examining the trade-offs between control, cost, and complexity.

The session will dive deep into multi-cloud deployment strategies, comparing platforms like Kubernetes, AWS ECS, and emerging solutions like dstack. Attendees will learn about queue-based scaling approaches and cost optimization techniques. The presentation will cover critical infrastructure decisions such as model storage options, container registry choices, and performance optimization strategies.

Through practical examples and performance benchmarks, the presentation will demonstrate how to build robust, cost-effective image generation services that can handle production workloads while maintaining flexibility across cloud providers.

This practical approach will help teams build reliable, enterprise-grade image generation services that can handle real-world production demands while avoiding common pitfalls and optimizing for both performance and cost-effectiveness.

Key Takeaways

• Multi-Cloud Deployment Strategy: Understand the pros and cons of different deployment options (managed APIs, serverless, self-hosted) and learn how to implement cost-effective multi-cloud solutions that avoid vendor lock-in while optimizing for performance and reliability.
• Production-Ready Scaling Techniques: Master queue-based scaling approaches and model optimization strategies that ensure consistent service performance at scale.

Target Audience Segment

This session is designed for DevOps Engineers, ML Engineers, and Technical Architects who are responsible for deploying and maintaining AI services in production environments. The content will be particularly valuable for teams currently using or planning to implement image generation services, cloud infrastructure professionals looking to optimize AI workloads, and technical decision-makers evaluating deployment strategies for GPU-intensive applications. Prior experience with containerization, cloud platforms, and basic ML concepts will be helpful but not required.