AI/ML Engineer

Persona

Optimizes machine learning models for energy efficiency and implements sustainable AI practices to reduce computational carbon footprint.

11 patterns

Architecture

Adopt serverless architecture for AI/ML workload processes

Building an ML model takes significant computing resources that need to be optimized for efficient utilization.

• ai
• machine-learning
• serverless
• size:small

Run AI models at the edge

Deploy AI inference on edge devices or local infrastructure to reduce data transfer, network energy use, and reliance on centralised cloud compute.

• ai
• compute
• deployment
• machine-learning
• networking
• size:large

Select a more energy efficient AI/ML framework

Training an AI model implies a significant carbon footprint. The underlying framework used for the development, training, and deployment of AI/ML needs to be evaluated and considered to ensure the process is as energy efficient as possible.

• ai
• machine-learning
• size:small

Select efficient accelerators and instance types for AI workloads

Match AI workloads to the most energy-efficient hardware accelerator or instance type to improve utilisation and reduce energy consumption per inference or training run.

• ai
• cloud
• compute
• machine-learning
• size:medium

Use energy efficient AI/ML models

Evaluate and use alternative, more energy efficient, models that provide similar functionality.

• ai
• machine-learning
• size:small

Use on-demand execution for AI and agent workloads

Trigger AI and agent workloads only when needed using serverless or event-driven platforms to eliminate idle compute and reduce unnecessary energy consumption.

• ai
• cloud
• compute
• machine-learning
• serverless
• size:medium

Development

Leverage pre-trained models and transfer learning

Fine-tune existing pre-trained models instead of training from scratch to dramatically reduce the compute, energy, and time required for model development.

• ai
• compute
• machine-learning
• size:medium

Optimize agent orchestration to reduce unnecessary model calls

Design agentic AI workflows to minimise redundant model invocations and unnecessary compute through caching, conditional logic, and efficient orchestration patterns.

• ai
• compute
• machine-learning
• size:medium

Optimize data storage formats for AI training and inference

Use efficient storage formats, compression, and indexing strategies for AI datasets and embeddings to reduce storage footprint, data transfer, and retrieval compute.

• ai
• machine-learning
• size:medium
• storage

Select efficient ML frameworks and inference runtimes

Choose ML frameworks and inference runtimes that best match your hardware and workload to reduce compute overhead and improve energy efficiency across training and production inference.

• ai
• compute
• machine-learning
• size:medium

Use right-sized and energy-efficient AI models

Select and optimize AI models that are appropriately sized for the task to reduce compute, memory, and energy consumption during training and inference.

• ai
• compute
• machine-learning
• size:medium