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FROM PYTHON TO MOJO: BUILDING FASTER ML APPLICATIONS: Leverage MLIR-powered compilation for AI inference, data pipelines, and compute intensive workloads
Build ML applications that stay fast from notebook to production with a clear path from Python to Mojo.
Prototypes move quickly in Python, then real workloads expose bottlenecks, memory pressure, and deployment friction. This book gives you a measured way to find the true hotspots, decide what stays in Python, and move critical kernels to Mojo without breaking your stack.
You get a practical workflow that links profiling to concrete changes, clean interfaces between languages, and packaging that others can run. Every technique is tied to code, benchmarks, and checks that make speed gains stick.
Profile effectively with cProfile, Scalene, and py-spy, read flamegraphs, and separate Python time from native time.Use a decision framework to keep code in Python, lean on high quality libraries, or port kernels to Mojo where it pays off.Set up reliable environments with pixi or uv, verify compilers and CUDA or ROCm, and work cleanly on Windows through WSL.Learn Mojo foundations that matter for performance, including types, control flow, error handling, ownership, borrowing, and generics.Iterate with JIT using mojo run, ship with AOT using mojo build, and pick flags and optimization levels that matter.Write SIMD friendly code, choose vector widths, handle alignment and tails, and combine SIMD with threads safely.Use Python and C interop that ships, including packaging Mojo as an importable module and using C ABIs for zero copy buffers.Bring GPUs online with Mojo kernels, understand indices, memory spaces, synchronization, and device support checks.Speed up data pipelines with Arrow memory maps, Polars and DuckDB vectorized transforms, and Parquet compression choices.Ship a small service, serve with MAX using OpenAI compatible routes, containerize, and benchmark with warmups, fixed seeds, and fair comparisons.This is a code heavy guide with working snippets and command lines that map directly to real projects, so you can measure gains on your own hardware.
Grab your copy today and turn working Python into fast, production ready ML.
Prototypes move quickly in Python, then real workloads expose bottlenecks, memory pressure, and deployment friction. This book gives you a measured way to find the true hotspots, decide what stays in Python, and move critical kernels to Mojo without breaking your stack.
You get a practical workflow that links profiling to concrete changes, clean interfaces between languages, and packaging that others can run. Every technique is tied to code, benchmarks, and checks that make speed gains stick.
Profile effectively with cProfile, Scalene, and py-spy, read flamegraphs, and separate Python time from native time.Use a decision framework to keep code in Python, lean on high quality libraries, or port kernels to Mojo where it pays off.Set up reliable environments with pixi or uv, verify compilers and CUDA or ROCm, and work cleanly on Windows through WSL.Learn Mojo foundations that matter for performance, including types, control flow, error handling, ownership, borrowing, and generics.Iterate with JIT using mojo run, ship with AOT using mojo build, and pick flags and optimization levels that matter.Write SIMD friendly code, choose vector widths, handle alignment and tails, and combine SIMD with threads safely.Use Python and C interop that ships, including packaging Mojo as an importable module and using C ABIs for zero copy buffers.Bring GPUs online with Mojo kernels, understand indices, memory spaces, synchronization, and device support checks.Speed up data pipelines with Arrow memory maps, Polars and DuckDB vectorized transforms, and Parquet compression choices.Ship a small service, serve with MAX using OpenAI compatible routes, containerize, and benchmark with warmups, fixed seeds, and fair comparisons.This is a code heavy guide with working snippets and command lines that map directly to real projects, so you can measure gains on your own hardware.
Grab your copy today and turn working Python into fast, production ready ML.
237 pages, Kindle Edition
Published October 5, 2025
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