Network and systems diagnostics are complex problems that are currently handled mostly through manually constructed procedures and knowledgebases. In spite of these problems being pervasive, current techniques are unable to resolve a significant fraction of them in acceptable turnaround time. Our objectives through the NetPrints, Déjà vu, and Baaz projects has been to hone in onto some aspects of these problems, and automatically find solutions through the use of various machine learning techniques in a timely manner. NetPrints and Déjà vu concentrate on building knowledgebases for diagnosing networking-related problems, while Baaz finds misconfigurations in systems access control, thereby mitigating the problem of insider attacks. In this talk, I will describe the objectives, design and evaluation of these systems and how they aid network and system administrators effectively find the root-cause of problems and fix them.