  Modern cloud has turned data services into easily accessible commodities.
With just a few clicks, users are now able to access a catalog of data
processing systems for a wide range of tasks. However, the cloud brings in both
complexity and opportunity. While cloud users can quickly start an application
by using various data services, it can be difficult to configure and optimize
these services to gain the most value from them. For cloud providers, managing
every aspect of an ever-increasing set of data services, while meeting customer
SLAs and minimizing operational cost is becoming more challenging. Cloud
technology enables the collection of significant amounts of workload traces and
system telemetry. With the progress in data science (DS) and machine learning
(ML), it is feasible and desirable to utilize a data-driven, ML-based approach
to automate various aspects of data services, resulting in the creation of
autonomous data services. This paper presents our perspectives and insights on
creating autonomous data services on Azure. It also covers the future endeavors
we plan to undertake and unresolved issues that still need attention.
