In-memory NoSQL transactional data grids are emerging as an attractive alternative to conventional relational distributed databases. In these platforms, replication plays a role of paramount importance, as it represents the key mechanism to ensure data durability. In this work we focus on Atomic Broadcast (AB) based certification replication schemes, which have recently emerged as a much more scalable alternative to classical replication protocols based on active replication or atomic commit protocols. We first show that, among the existing AB-based certification protocols, no “one-fits-all” solution exists that achieves optimal performance in presence of heterogeneous workloads. Next, we present PolyCert, a polymorphic certification protocol that allows for the concurrent coexistence of different certification protocols, relying on machine-learning techniques to determine the optimal certification scheme on a per transaction basis. We design and evaluate two alternative oracles, based on parameter-free machine learning techniques that rely both on off-line and on-line training approaches. Our experimental results demonstrate the effectiveness of the proposed approach, highlighting that PolyCert is capable of achieving a performance extremely close to that of an optimal non-adaptive certification protocol in presence of non heterogeneous workloads, and significantly outperform any non-adaptive protocol when used with realistic, complex applications that generate heterogeneous workloads.