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This book presents the essential technologies that extend Python's capabilities for production AI/Data Science. It argues that modern systems require a polyglot architecture where Python orchestrates workflows, but performance-critical components are built with specialized tools: Rust for safe systems programming, Julia for numerical computing, Mojo for AI infrastructure, high-performance databases (OLAP/Vector), Apache Arrow for zero-copy data exchange, CUDA/GPUs for parallel compute, and Kubernetes for scalable orchestration. The guide provides a roadmap for data professionals to build robust, efficient, and scalable systems by selecting the right tool for each task in the stack.
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