Make AI Workflows 2x Faster and 6x Cheaper with Functional Python (Session for Asia & Australia)

Functional programming for AI efficiency: learn how pure functions, persistent memoization, and deterministic parallelization can speed up ML workflows and reduce compute spend without new hardware.

In this webinar, host Sharice Mayer talks with Vlad Pavlov, a hands-on AI engineering leader and open-source enthusiast, with 20+ years at startups to Microsoft & Intel. Vlad shares practical patterns for AI engineers and data teams to speed up experiments, reduce cloud spend, and reuse results across teams using pure functions, memoization, and a shared cache.

If you build data-intensive AI or ML systems and want more throughput without more hardware, this talk is for you.

🔥 Key Highlights:
– Why compute & electricity are the biggest bottlenecks for AI
– How functional programming ideas improve ML workflows
– Memoization and persistent caching to cut training costs
– Turning idle enterprise servers into AI accelerators
– Using Python libraries (like Pythagoras) to boost efficiency
– What enterprises can learn from human brain computation
– Future vision: planet-scale execution with global caches

👥 Speaker, Host, and Organiser:
– Vlad Pavlov – Senior Director, AI & Software Engineering at PMG:   / vlpavlov  
– Sharice Mayer – ML & Data Scientist at Aerendir:   / sharice-mayer  
– Dmytro Spodarets – Founder & CEO at Data Phoenix:   / spodarets  

🔗 Helpful Resources:
– Slides and Colab demos: https://docs.google.com/presentation/...
– Pythagoras: https://pypi.org/project/pythagoras/

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