Green Machine Learning (ML) comes with low carbon footprint, small sizes of models, low computing overhead as well as transparency and interpretability features. 

In the design and analysis of sustainable systems where various objectives are considered and need to be prudently balanced, ML environment assumes a visible role. Furthermore, while the development of ML architectures becomes focused on large volumes of data, a growing role of knowledge in the design process has to be fully acknowledged.  From their usage perspective in the ML learning environment, data and knowledge are inherently different. Data are numeric and precise. Knowledge is general and usually expressed at the higher level of abstraction bringing an aspect of information granularity.

Having this in mind, we introduce a general concept of knowledge landmarks (knowledge anchors). The landmarks provide a vehicle that along with data navigate the design of ML models. The role of knowledge is two-fold. First, it fills data gaps existing in the input space. Second, it serves as a regularization term that is essential in avoiding constructing physically infeasible models.  In the design process, we elaborate on two fundamental issues, namely (i) we discuss an origin and construction of knowledge landmarks, and (ii) we present a realization of learning mechanisms in the presence of data and knowledge. Main ways of elicitation of knowledge landmarks are identified and discussed, in particular, we elaborate on techniques based on large and diversified amounts of data collected in the past and encapsulated in the form of prototypes (formed through clustering techniques). Along this line of knowledge acquisition, the relevance of the landmarks is quantified through their granularity giving rise to granular knowledge landmarks. Another alternative of building knowledge landmarks is based on their acquisition through LLMs; in this case they are qualitative and described as symbols. The constraints imposed on the data-based ML model are quantified through a collection of magnitude and change of magnitude landmarks. Once knowledge landmarks have been acquired, a unified data-knowledge environment is constructed. In virtue of the low number of knowledge landmarks, the knowledge regularization is formalized in the form of the Gaussian Process regression where the probabilistic information granules are included in the minimization of the augmented loss function.

Detailed illustrative studies are showcased using rule-based architectures.