In addition to the infra management, it's quickly tricky to support qa/viz/tuning/debugging tools for very different sorts of algorithms/outputs/configurations/metrics. Do you see your project going in those directions?
For short-term roadmap, I am trying to work on stability, it's very hard to have default values since you don't know how it will be used, e.g. on Minikube or by a team scheduling a lot of parallel experiments, so what I am trying to do is at least having an automatic or simple way to scale workers responsible for scheduling, hyper params tuning, and monitoring.
For tuning, the platform will keep supporting some algorithms to automate the hyper params search, maybe introducing more priors for the Bayesian optimization, I also think more tests are needed to validate the behavior of the Bayesian optimization and Hyperband.
For visualization, currently, you can start a Tensorboard for any project created on the platform, but there are some problems with this assumption, if the project has a lot of experiments, Tensorboard becomes slow to irresponsive. Next release will introduce the possibility to create Tensorboard jobs per experiment or per hyper tuning experiment group, and possibly any collection of experiments to compare them.
The platform collectes already metrics from experiments, so a basic visualization is also planned to have a quick overview before diving into a Tensorboard.
And most importantly, I think there are some usability issues that need to be solved to make the experience better.
There are also a couple of ideas around team collaboration that will be introduced in the mid term.