Data is the fuel behind machine learning solutions but also its biggest weakness. The dependency on large labeled datasets makes many machine learning processes completely unpractical. How can organizations address this challenge? This session presents a series of techniques that can help companies build machine learning solutions in the absence of large labeled datasets. Exploring methods such as semi-supervised, weakly-supervised or reinforcement learning to different privacy techniques, we explore patterns and neural network architectures that work efficiently in scenarios without large labeled datasets. To keep things practical we will discuss several case studies that illustrate how these methods are being used in real-world machine learning solutions today.