The Hidden Costs of AI: Optimizing Your ML Data Stack for Production Scale

The promise of AI at scale is undeniable, but hidden costs often undermine efficiency and ROI. From fragmented data pipelines to resource-intensive infrastructure, these unseen challenges can escalate expenses and delay production goals.

Join this executive-level webinar to uncover the true cost of scaling AI and Machine Learning. Our panel of experts will share actionable strategies to optimize your ML data stack, ensuring streamlined workflows, efficient resource allocation, and long-term scalability. Discover how to align infrastructure and processes to maximize performance while controlling costs.

Key Takeaways:

• Exposing Hidden Inefficiencies: Identify overlooked challenges in ML pipelines, data workflows, and production environments.
• Optimizing AI Infrastructure: Best practices for designing scalable, cost-effective data stacks that meet production demands.
• Driving Business Value: Real-world examples of AI architectures that balance performance, cost, and ROI at scale.

Panelists to be announced soon

Host or Publisher Data Science Connect