The Machine Learning (ML) technology has taken the world by storm since it equipped the machines with previously unimaginable decision-making capabilities. However, building powerful ML models is not an easy task, but the demand for their utilization in different industries and areas of expertise is high. This was recognized by entities that have managed to create ML models and they started offering ML prediction services to clients in exchange for financial compensation. In this work, we explore how a ML predication service platform can be built in which we focus on two things: (1) privacy-preservation which entails keeping the client’s datasets and service provider’s ML models private and (2) inference verifiability ensuring that the ML prediction service providers do not commit fraud. The result are two platforms: ML Prediction Service Platform (MLPSP) which does not protect the secrecy of the client’s datasets but offers model privacy and verifiability of the predictions and Input-Privacy ML Prediction Service Platform (IP-MLPSP) which protects the secrecy of the client’s dataset and model privacy but the verifiability is probabilistic.