  Recently, the database management system (DBMS) community has witnessed the
power of machine learning (ML) solutions for DBMS tasks. Despite their
promising performance, these existing solutions can hardly be considered
satisfactory. First, these ML-based methods in DBMS are not effective enough
because they are optimized on each specific task, and cannot explore or
understand the intrinsic connections between tasks. Second, the training
process has serious limitations that hinder their practicality, because they
need to retrain the entire model from scratch for a new DB. Moreover, for each
retraining, they require an excessive amount of training data, which is very
expensive to acquire and unavailable for a new DB. We propose to explore the
transferabilities of the ML methods both across tasks and across DBs to tackle
these fundamental drawbacks.
  In this paper, we propose a unified model MTMLF that uses a multi-task
training procedure to capture the transferable knowledge across tasks and a
pre-train fine-tune procedure to distill the transferable meta knowledge across
DBs. We believe this paradigm is more suitable for cloud DB service, and has
the potential to revolutionize the way how ML is used in DBMS. Furthermore, to
demonstrate the predicting power and viability of MTMLF, we provide a concrete
and very promising case study on query optimization tasks. Last but not least,
we discuss several concrete research opportunities along this line of work.
