Machine Learning has established itself at the core of the business models of outstanding companies, and the society in general is quickly taking advantage of this technology in a wide variety of application areas. Deep Learning is the key to the expansion of Machine Learning and Artificial Intelligence, in the last years. However, Deep Learning methods are criticized because of their black-box nature, which seriously limits their interpretability, and their inability to handle model uncertainty (i.e. to know what they don’t know). These issues are preventing the adoption of this technology in many critical applications such as medical diagnosis, where doctors (and patients) demand explanations about why this model is making this prediction and, also, models which do not provide precise answers when they are asked to solve a task they have not been specifically trained for. Addressing these issues will make this technology safer, more trustworthy and, in consequence, much more adopted by society. The DeeProb project intends to pave the way to the next generation of Machine Learning methods by introducing Deep Probabilistic Modeling. By appropriately developing a probabilistic component and relying on Bayesian statistics, we plan to solve the above- mentioned drawbacks of Deep Learning while keeping the effectiveness of those models and producing scalable methodologies for inference and learning. All the developments will be made available to the community as open source software tools. The new methods will be instantiated and tested in two use cases with remarkable impact, respectively, in genomics and ecology: prediction of gene duplicability in plants and rural abandonment forecasting.