PyCon Israel 2022
| title | author | length |
|---|---|---|
| Life, Death, and Shopping | Dina Bavli | 35m |
Step-by-step purchase prediction implemented in PySpark that turns terabytes of retail clickstream into labeled examples by generating moving model run dates… | ||
| Why Does “Don’t Stop Me Now” by Queen Make Us Happy? Feature Analysis | Noga Karni | 21m |
An enriching talk about music theory and analysis using python tools.
Have you ever noticed that, depending on your mood, not every song is suitable for you… | ||
| What are we busy about? | Layla Abu Khalaf | 21m |
Automated scheduling pipeline uses CP-SAT via OR-Tools with Pandas and NumPy to encode availability, state medical licenses and labor constraints, then emits… | ||
| Data Class Serialization The Right Way | Bat El Ziony Sabati, Mordechai Alter | 33m |
Do you ever use data classes in your project? Need to store these data structures for later use?
In our talk, we will present how to do it in Python. We will… | ||
| Web apps for data science using streamlit | Liron Soffer | 21m |
Nobody cares about your algorithm, learn how to communicate model insights.
Data scientists can lose track of time working on their models, failing to… | ||
| Formal Verification through Python – Why and How? | Avraham Raviv, Or Reginiano, Eliya Bronshtein | 21m |
Formal verification (FV) can prove the correctness of algorithms and systems and so ensure safety. Since FV tools are not easy to use, we will show examples… | ||
| 🤖 Transformer-based NLP Pipelines with SpaCy v3 | Stav Shemesh | 21m |
Shows how spaCy v3 plugs transformer models (BERT/Roberta family) into pipelines and how replacing separate task encoders with a shared Transformer listener… | ||
| Don’t Underestimate the Obvious: Murphy’s Law in Real-life Data Science | Inon Peled | 22m |
Models break because data and process details break: a weather model trained only on summer data fails in autumn, HTML scraping collects “for sale” pages as… | ||
| Getting Started with OpenTelemetry in Python | Michael Haberman | 23m |
In this talk, you will learn about the concept and benefits of tracing by examining the open-source project OpenTelemetry. You will leave this session knowing… | ||
| How to Lift Your Tech Debt Curse with the Magic of the Open-Close Principle | Dor Amram | 19m |
Claim: apply the Open-Closed Principle to refactor fragile Python production code without changing behavior, using small, tested steps. The code example… | ||
| Leveraging networkx in-memory graphs for securing your cloud infrastructure | Naor David | 38m |
Checkov uses networkx digraph to build an in-memory DAG of Terraform and other IaC blocks, replacing a slow Neptune experiment and enabling fast graph queries… | ||
| JSON - The Fine Print | Miki Tebeka | 24m |
In this talk we'll discuss the finer points of working with JSON. We'll cover custom serialization, validation, and shine some lights at some darker… | ||
| Overcoming Concurrency Issues in Web Applications | Haki Benita | 25m |
Concurrency in web applications is hard to identify and debug, and very easy to get wrong! In this talk I'm going to present common concurrency issues and… | ||
| Basic microcontroller programming with Python | Anat Wax | 22m |
A working water-level monitor built with an Arduino Uno with pySerial and pyFirmata reads an analog water sensor on analog pin A0 (raw 0–641), sends values at… | ||
| Django URL Pattern Role Authorization System | Yarin Asulin | 15m |
Taking the Django traditional groups and permissions to the next level by adding layer and using an access endpoint pattern approach to provide scalability… | ||
| Minimum Viable Security for Python Applications | Michael Segal | 20m |
Minimum Viable Security installs five concrete CI/CD controls — Bandit for Python SAST, Gitleaks for secret scanning (including commit history), Python… | ||
| Pythonic DDD And How To Improve Your Life When Maintaining A Monolith | Niv Sluzki | 21m |
Startups choose python because it helps you to set up your application very quickly, but after a few months, it can get really messy. This is the story of how… | ||
| Fine Grained Error Locations in Tracebacks | Ron Alfia | 20m |
Python 3.11 embeds fine-grained error location metadata in bytecode so tracebacks show exact column positions inside a source line, not just the line number… | ||
| Under the sea - Attacking vulnerable C creatures in Snakes-land | Aviad Hahami | 21m |
Claim: many PyPI packages silently bundle C sources that carry real CVEs. Using snippet matching tied C code to CVEs and a vulnerability DB, researchers unpack… | ||
| Memoirs of a Python Object: Memory Management and Improving Performance | Michael Khaitov | 23m |
Shows that CPython objects are C structs (the PyObject struct) with a reference count and type info, and that reference counting plus a generational tracing GC… | ||
| Identity providers, your app, and everything in-between | Hodaya Ankri | 20m |
Shows how a Flask app registers with Microsoft Azure, redirects users to Microsoft’s login, exchanges the returned code for id, access, and refresh tokens via… | ||
| Optimizing Code Performance for Python Internals | Yonatan Goldschmidt | 42m |
Shows concrete CPython internals that affect speed: CPython 3.10 uses a faster lookdict_unicode path for string-only dicts but inserting a non-string key or… | ||
| The Journey of Upgrading A Python Version: From a Debugger Perspective | Nathan Shain | 19m |
Upgrading a Python debugger for 3.10 forces code-level fixes: CPython replaces the old line table with PEP 626 and removes the legacy struct field, the new… | ||
| From PyPerf to py-spy - Everything You Need to Know About Python Profilers | Yishai Zinkin | 21m |
Compares deterministic call-tracing with sampling via stack traces and shows how flamegraphs turn millions of samples into actionable hotspots. Describes… | ||
| It's Critical: Concurrent programming as a sane programming model | David Baum | 20m |
Builds a toy async-await framework from generators, non-blocking sockets and the OS selector, implementing futures and tasks and a simple event loop that… | ||
| Building your own dystopic surveillance state with Python | Adam Kariv | 21m |
Step-by-step demo of face detection and recognition in Python using the face_recognition library (author calls it mediocre) plus OpenCV models: detect faces… | ||
| Python strategy panel | Michael Czeizler, Barak Peleg, Orit Wasserman, Lior Mizrahi, Oren Nissenbaum, Miki Tebeka, Tal Sayhonov | 53m |
Companies report Python as the dominant language for backend, data pipelines and ML while only low‑latency vehicle chips, frontends and some Kubernetes… | ||
| The hidden costs of your favorite functions | Maya Gershovitz Bar | 20m |
Shows that calling pop(0) shifts the whole list so pop(0) is O(n) while pop() is O(1), measured as ~20x slower on 100k items and >100x on 1M items… | ||
| Building Lightning-Fast Apps With asyncio | Assaf Dayan | 18m |
Replaces an eight-node legacy Java cluster with one asyncio-powered instance that matches and often exceeds prior throughput, handling around 150 million… | ||
| What happens when you import a module? | Reuven M. Lerner | 45m |
Explains exactly how import works: Python checks sys.modules cache, walks sys.meta_path finders (pathfinder, builtins, frozen), gets a ModuleSpec, uses the… | ||
| Monorepo - One Repo To Rule Them All | Yael Green | 22m |
Company consolidates ~dozens of repos into a single monorepo using the to-mono script (modified to handle non-master branch names) and preserves history by… | ||
| No more sassy SaaS integrations | Amit Ripshtos | 18m |
A lightweight SDK is built that splits integrations into a stream phase (incremental fetch, pagination, rate-limit and state handling) and a parse phase… | ||
| Python common security mistakes in 2022 | Gil Cohen, Eyal Greenberg | 45m |
Live demos reproduce real CYE pen tests showing parameter tampering that lets one user read and edit another’s account, classic SQL injection that pulls credit… | ||
| Supercharging Pipeline Efficiency with ML Performance Prediction | Boaz Wiesner, Keren Meron | 25m |
Singular builds a pipeline that predicts task runtime and memory to split hundreds of thousands of daily Celery tasks into light and heavy queues (>90% light… | ||
| Detecting anomalous sequences using text processing methods | Liron Faybish | 17m |
Pipeline converts sequences to vectors with Word2vec + RNN autoencoder or to a single-vector with Bert + sentence vector + autoencoder, implemented in Python… | ||
| Zero to Hero: Few Shot Learning + Multi-armed Bandit | Yoel Zeldes, Shuki Cohen | 45m |
Join us to learn how to use large language models to solve NLP tasks. Via live coding, we'll demonstrate how to use Few Shot Learning together with Multi-armed… | ||
| Python & DAG architecture: Winning combination for dev of complex algo agile flows | Noa Marom | 26m |
Rapid development of complex algorithms requires an agile management. This talk will demonstrate how we leverage Python flexibility and DAGs power to enable a… | ||
| Cracking Wordle: Machine Learning based Strategies | Efrat Ravid | 24m |
Compares three concrete solvers: a random baseline (4.9 average guesses), a letter-frequency heuristic (4.48), a reinforcement learning agent trained with a… | ||
| python at scale – creating a High Definition Map of the world's road network | Amit Raphael | 47m |
How we map continents at cm level accuracy from crowd sourced computer vision data using PySpark.
A tale of engineering challenges working with python at huge… | ||
| Easily reduce Deep Computer Vision to shallow NLP | Assaf Klein, Hila Weisman Zohar | 20m |
Being able to classify images is at the heart of many recommender systems. In this talk, we will share a simple trick to make the task of building an image… | ||
| Circuit Playground Bluefruit Board: Bringing Objects to Life (With Python Code) | Lior Dagan Leib | 24m |
A round Adafruit Circuit Playground Bluefruit board runs tiny Python code over USB or battery to drive 10 NeoPixels, a speaker and sensors (light, temperature… | ||
| Hidden Hacks in Linters for Better & More Secure Code | Gabriel Manor-Liechtman | 20m |
Linters are a great tool that enable developers to create static analysis rules for their code base, and the most popular one in the Python ecosystem is Pylint… | ||
| Property Based Testing with Hypothesis: Stronger Tests, Less Work | Shai Geva | 41m |
Shows how Python’s Hypothesis generates hundreds of randomized cases, shrinking failures to minimal examples, and finds real bugs in a small CRUD app by… | ||
| GPT-3 wrote the description for this talk... Scary or exciting? | Ronnie Sheer | 21m |
Shows how to wire Python to OpenAI and AI21 APIs and DALL·E, using zero-shot and few-shot prompts, prompt cleaning, optional fine-tuning and a FastAPI backend… | ||
| Effective Protobuf: Everything You Wanted To Know, But Never Dared To Ask | Liran Haimovitch | 23m |
Communicating and persisting data (and state!) is at the very core of software engineering. That’s where serialization comes in - but getting it right can be… | ||
| There is always another way: Sharpen your NumPy skills with the 8 Queens puzzle | Ariel Lieberman | 22m |
Walks through five concrete NumPy implementations to check diagonals for all 40,320 permutations (8!) and finds the 92 valid boards, comparing timings: initial… | ||
| meet the best feature in python 3.10: match-case | Dror Ivry | 18m |
A subtle bug where lines like "1,1 == 1,1" and tuples such as "1, True, 1" are parsed as tuples rather than comparisons leads to incorrect boolean logic… | ||
| Exploring the Cheese Shop - What's in the Python Package Index? | Michael Sverdlin | 23m |
We pip install packages all day long, but did you consider where it is coming from?
Let's explore Pypi, the python package index. Topics we will cover:
1… | ||
| Off road profiling - when the automated profilers just don't cut it. | Shachar Shemesh | 24m |
Automatic profilers slow a 9‑hour CI job enough to make iteration impossible, so a lightweight manual profiler is built instead: manprof inserts time.time… | ||
| nothing matches | ||