"Easy Python": lies, damned lies, and metaclasses
Grigory Petrov, Maxim Danilov
Best Practice, Coding / Code-Review, Development Methods

top-10 Python complexities and how they are required to fight the "software complexity problem" in big projects

Demystifying Python's Internals: Diving into CPython by implementing a pipe operator
Sebastiaan Zeeff
Python - CPython new features, Python fundamentals

Do you want to dive into the CPython Source Code but feel a bit overwhelmed? Watch Sebastiaan Zeeff demystify CPython's Internals by taking you through the implementation of a new operator.

Do I need to be Dr. Frankenstein to create real-ish synthetic data?
Data Engineering, Ethics (Privacy, Fairness,… ), Governance

Synthetic data not only address the privacy needs but also offer workaround for unprecedented situations. This talk introduces their different types, the options for their generation, and how you don't need to be a mad scientist to make realistic synthetic data

Optimize your network inference time with OpenVINO
Adrian Boguszewski
Jupyter, Neural Networks / Deep Learning, Performance

Learn how to automatically convert the model using Model Optimizer and how to run the inference with OpenVINO Runtime to infer your model with low latency on the CPU and iGPU you already have. The magic with only a few lines of code.

Squirrel - Efficient Data Loading for Large-Scale Deep Learning
Dr. Thomas Wollmann
Distributed Computing, Neural Networks / Deep Learning, Parallel Programming / Async

Learn why we built and open sourced a data infrastructure library for deep learning.