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Python

AI, data, and automation

Python powers my AI and data engineering work. From training pipelines to data processing, I build production Python systems that are maintainable and performant.

8+years in production
60+projects shipped
expertproficiency

My take

Why I use Python

Python is the default for AI work because that's where the libraries, papers, and community live. I lean on it heavily for ML pipelines, data processing, and any task where the cost of writing code matters more than the cost of running it.

Want the broader stack philosophy? Read about how Sri picks tools or browse engineering insights.

Honest assessment

Strengths & tradeoffs

No tool is perfect. Here's what shines and what to watch for.

Strengths

  • Unmatched AI/ML and data ecosystem (PyTorch, NumPy, pandas)
  • Readable, expressive syntax - fast to write, fast to onboard
  • Strong scripting and prototyping ergonomics
  • Excellent scientific computing tools
  • Huge community and learning resources

Tradeoffs (honestly)

  • GIL limits true CPU parallelism without multiprocessing
  • Runtime performance is slow vs. compiled languages
  • Dependency management has historically been painful (pip vs. poetry vs. uv)
  • Dynamic typing requires discipline at scale (mypy/pyright help)

Fit assessment

When to reach for Python

Pick the right tool for the job.

Best fits

ML training and inference pipelines

Data engineering and ETL workflows

Quick automation and glue scripts

Scientific computing and research code

AI agents and LangChain applications

Not ideal for

CPU-bound, ultra-low-latency services

Tiny memory footprint deployments

Mobile or browser-native applications

Common use cases

AI/ML pipelinesData engineeringAutomation scriptsAPI backends

Stack

Pairs well with Python

Tools and platforms I commonly combine with this one.

Need help with Python?

Whether you're starting fresh or optimizing an existing implementation, I can help you get the most out of this technology. Read more in insights or get in touch.

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