  Hybrid intelligence aims to enhance decision-making, problem-solving, and
overall system performance by combining the strengths of both, human cognitive
abilities and artificial intelligence. With the rise of Large Language Models
(LLM), progressively participating as smart agents to accelerate machine
learning development, Hybrid Intelligence is becoming an increasingly important
topic for effective interaction between humans and machines. This paper
presents an approach to leverage Hybrid Intelligence towards sustainable and
energy-aware machine learning. When developing machine learning models, final
model performance commonly rules the optimization process while the efficiency
of the process itself is often neglected. Moreover, in recent times, energy
efficiency has become equally crucial due to the significant environmental
impact of complex and large-scale computational processes. The contribution of
this work covers the interactive inclusion of secondary knowledge sources
through Human-in-the-loop (HITL) and LLM agents to stress out and further
resolve inefficiencies in the machine learning development process.
