Ville Tuulos - Compute anything with Metaflow | PyData Global 2023

The compute landscape is becoming increasingly heterogeneous, with multiple paradigms coexisting (vertical scaling, horizontal scaling, GPUs) rather than one universal solution

Cloud computing has evolved from basic EC2 instances to sophisticated options including specialized GPU providers, making compute more accessible and flexible

Vertical scalability (bigger machines) is often more efficient than horizontal scaling for many Python workloads, thanks to increasingly powerful cloud instances

Modern Python frameworks (NumPy, Pandas, DuckDB, etc.) can handle surprisingly large workloads on single nodes, challenging the need for distributed computing in many cases

The emergence of AI/ML workloads, particularly LLMs, is driving unprecedented demand for compute resources and new hardware architectures

Organizations are becoming more sophisticated about cloud costs and seeking optimal solutions rather than defaulting to distributed computing

Metaflow focuses on developer experience by abstracting infrastructure complexity while allowing flexible resource allocation based on workload needs

The Python ecosystem has matured significantly with high-performance libraries and frameworks that can efficiently utilize modern hardware

Task parallelism (running different operations on same data) has emerged as an important compute pattern alongside data parallelism

Cloud providers are increasingly competing on specialized hardware and services, giving users more options for cost-effective compute