Over the past few years, we have witnessed how AI has improved at an unprecedented speed. Improvements in processor speed and the arrival of big data have revolutionized the field. Models such as the recently released GPT-3 and Turing-NLG have achieved impressive results that raised awareness of the capabilities of AI. These models follow the current trend of building bigger models to achieve better results. However, developing a bigger model does not always imply a better model. Factors such as computational resources, memory, and power to train and run models play an essential role in the future of AI. Tiny machine learning is a discipline emerging in the field that focuses on building Machine learning solutions bearing in mind these crucial factors. TinyML has the potential to transform many industries. In the session, we will introduce TinyML; we will dive deep into how it works, how to implement it, as well as some interesting use cases.