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Cache management policies are responsible for selecting the items that should be kept in the cache, and are therefore a fundamental design choice for obtaining an effective caching solution. Heuristic approaches have been used to identify access patterns that affect cache management decisions. However, their behavior is inconsistent, as they can perform well for certain access patterns and poorly for others. Given machine learning’s (ML) remarkable achievements in predicting diverse problems, ML techniques can be applied to create a cache management policy. Yet a significant challenge arises from the memory overhead associated with ML components. These components retain per item information and must be invoked on each access, contradicting the goal of minimizing the cache’s resource signature. In this work, we propose ALPS, a light-weight cache management policy that takes into account the cost of the ML component. ALPS combines ML with traditional heuristic-based approaches and facilitates learning by identifying several statistical features derived from space-efficient sketches. ALPS’s ML process derives its features from these sketches, resulting in a lightweight and highly effective meta-policy for cache management. We evaluate our approach over real-world workloads run against five popular heuristic cache management policies as well as a state-of-the-art ML-based policy. In our experiments, ALPS always obtained the best hit ratio. Specifically, ALPS improves the hit ratio compared to LRU by up to 20%, Hyperbolic by up to 31%, ARC by up to 9% and W-TinyLFU by up to 26% on various real-world workloads. Its resource requirements are orders of magnitude lower than previous ML-based approaches.