Kravchenko + Doest- Building highload ML powered service | PyData NYC 2022

Описание: Many engineers tend to think Python-based backends are not a good solution for highload services. We can't agree and would like to share our experience making it swift and robust. Some of the practices include:
labeling optimization based on fast clustering;
inference service design;
cascades of smart caching, rule-based heuristics, and heavy ML;
using Rust for optimizing most latency-affecting functions;

Overall, we'd like to share the principles we follow while designing the system - keeping it fast, maintainable, and easy to change.

The speech will be interesting for those who design ML-related systems oriented to high load and availability.

Bios:
Arseny Kravchenko
I'm a machine learning engineer, delivering ML projects since 2015 in individual contributor and leadership roles, mainly focusing on deep learning and ML ops-related problems.

Thymo ter Doest
ML engineer at Ntropy | Co-founder at AccountingBox | MSc Machine Learning at UCL

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