  The scale and complexity of workloads in modern cloud services have brought
into sharper focus a critical challenge in automated index tuning -- the need
to recommend high-quality indexes while maintaining index tuning scalability.
This challenge is further compounded by the requirement for automated index
implementations to introduce minimal query performance regressions in
production deployments, representing a significant barrier to achieving
scalability and full automation. This paper directs attention to these
challenges within automated index tuning and explores ways in which machine
learning (ML) techniques provide new opportunities in their mitigation. In
particular, we reflect on recent efforts in developing ML techniques for
workload selection, candidate index filtering, speeding up index configuration
search, reducing the amount of query optimizer calls, and lowering the chances
of performance regressions. We highlight the key takeaways from these efforts
and underline the gaps that need to be closed for their effective functioning
within the traditional index tuning framework. Additionally, we present a
preliminary cross-platform design aimed at democratizing index tuning across
multiple SQL-like systems -- an imperative in today's continuously expanding
data system landscape. We believe our findings will help provide context and
impetus to the research and development efforts in automated index tuning.
