It started from one question: machine learning is so useful, could it do something for the planet? Azure Machine Learning makes building a model much simpler.
This was the Microsoft Learn Student Ambassadors Social Impact Project for the first quarter of 2022, in the Green Tech track. The three of us lived in different countries, found times on Teams, and wrote a series of Dev.to posts together called The Green Daily.
Six posts
Post 1: introduction. What machine learning is, what Azure Machine Learning is, and some green ideas.
Post 2: two ways of doing it. The traditional way means NumPy, Keras, OpenCV and a lot of code; Azure Machine Learning offers low-code and no-code options.
Post 3: predicting forest cover type. Using Automated ML to predict which kind of trees a piece of land supports, from its terrain and soil. The English title was Which tree to plant?, and it opened with a little story.
Post 4: predicting cotton prices with the drag-and-drop Designer.
Post 5: recognising crops. Knowing where crops are grown and how much matters; this one used the Python SDK.
Post 6: start your green journey. Free student credits, how to choose between the three tools, and where to keep learning.
What I did
We split the posts between us, and I wrote the one on using machine learning in agriculture. While we planned, Manab offered to finish the green tech part “but I’d just need a few topics that we decide on”; Prasansha reminded us, “we need to mold all with Azure ML services.”