What factors are contributing to the unexpected 50% increase in compute costs for Owkin's AI model training pipeline this month?

Introduction

The unexpected 50% increase in compute costs for Owkin's AI model training pipeline this month is a critical issue that demands immediate attention. This surge in costs could significantly impact the company's bottom line and potentially hinder future AI development efforts. To address this problem, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term and long-term implications.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent change in the training pipeline. Has there been any significant update to the AI model architecture or training process in the past month?

Why it matters: Recent changes could directly correlate with the cost increase. Expected answer: Yes, we implemented a new deep learning framework. Impact on approach: If confirmed, we'd focus on the new framework's efficiency and resource utilization.

• Considering the scale of increase, I'm wondering about data volume changes. Has there been a substantial increase in the amount or complexity of data being processed by the AI models?

Why it matters: Data volume directly impacts compute requirements. Expected answer: We've onboarded several new large datasets recently. Impact on approach: If true, we'd need to evaluate data preprocessing and feature selection strategies.

• Given the nature of AI training, I'm curious about potential changes in model complexity. Have there been any modifications to the model architecture, such as increasing layers or parameters?

Why it matters: More complex models require more computational resources. Expected answer: We've been experimenting with larger, more sophisticated models. Impact on approach: This would lead us to focus on model optimization techniques and potential trade-offs between model size and performance.

• Thinking about infrastructure, I'm considering potential changes in compute resources. Has there been any shift in the cloud provider or instance types used for training?

Why it matters: Different providers or instance types can have varying cost structures. Expected answer: We recently migrated to a new cloud provider. Impact on approach: If confirmed, we'd need to review the new provider's pricing model and optimize resource allocation.