  The large data volumes expected from the High Luminosity LHC (HL-LHC) present
challenges to existing paradigms and facilities for end-user data analysis.
Modern cyberinfrastructure tools provide a diverse set of services that can be
composed into a system that provides physicists with powerful tools that give
them straightforward access to large computing resources, with low barriers to
entry. The Coffea-Casa analysis facility (AF) provides an environment for end
users enabling the execution of increasingly complex analyses such as those
demonstrated by the Analysis Grand Challenge (AGC) and capturing the features
that physicists will need for the HL-LHC.
  We describe the development progress of the Coffea-Casa facility featuring
its modularity while demonstrating the ability to port and customize the
facility software stack to other locations. The facility also facilitates the
support of batch systems while staying Kubernetes-native. We present the
evolved architecture of the facility, such as the integration of advanced data
delivery services (e.g. ServiceX) and making data caching services (e.g.
XCache) available to end users of the facility. We also highlight the
composability of modern cyberinfrastructure tools. To enable machine learning
pipelines at coffee-casa analysis facilities, a set of industry ML solutions
adopted for HEP columnar analysis were integrated on top of existing facility
services. These services also feature transparent access for user workflows to
GPUs available at a facility via inference servers while using Kubernetes as
enabling technology.
