AI-powered Climate Scenarios and Forecasts

The objectives

• Scalability: The need for infrastructure that supports increasing workloads and AI-driven models. 

• Cost Optimization: Reducing operational costs while maintaining performance. 

• Innovation Acceleration: Deploying advanced ML models efficiently to stay ahead in AI-powered climate forecasting. 

The solution

AI-Driven MLOps on AWS

As part of the migration, TCDF is moving its deep learning models—including Unet, Vision Transformer, and super-resolution generative models like Ensemble Diffusion—to AWS. The goal is to design a scalable, secure, and cost-efficient MLOps architecture, enabling seamless AI integration in climate forecasting. 

The results

• Enhanced Infrastructure : A secure, scalable, and ML-optimized AWS environment supporting HPC workloads.
• Faster Time to Market : Automated pipelines accelerate forecasting model deployment
• Cost Efficiency : AWS-powered infrastructure ensures optimized resource allocation and lower costs.
• Increased Innovation : Strengthened R&D capabilities with AI-driven forecasting solutions.
• Market Leadership : TCDF reinforces its position as a leader in AI-based climate services, boosting credibility and market presence.