  Document understanding is a key business process in the data-driven economy
since documents are central to knowledge discovery and business insights.
Converting documents into a machine-processable format is a particular
challenge here due to their huge variability in formats and complex structure.
Accordingly, many algorithms and machine-learning methods emerged to solve
particular tasks such as Optical Character Recognition (OCR), layout analysis,
table-structure recovery, figure understanding, etc. We observe the adoption of
such methods in document understanding solutions offered by all major cloud
providers. Yet, publications outlining how such services are designed and
optimized to scale in the cloud are scarce. In this paper, we focus on the case
of document conversion to illustrate the particular challenges of scaling a
complex data processing pipeline with a strong reliance on machine-learning
methods on cloud infrastructure. Our key objective is to achieve high
scalability and responsiveness for different workload profiles in a
well-defined resource budget. We outline the requirements, design, and
implementation choices of our document conversion service and reflect on the
challenges we faced. Evidence for the scaling behavior and resource efficiency
is provided for two alternative workload distribution strategies and deployment
configurations. Our best-performing method achieves sustained throughput of
over one million PDF pages per hour on 3072 CPU cores across 192 nodes.
