PyCon APAC 2019
as the organisers put it
"PyCon APAC is a volunteer-run, not-for-profit annual conference centered around the Python Programming language. The goal of the conference is to provide a venue where the Python programming language and surrounding technologies can be explored, discussed and exercised. PyCon APAC is the regional counterpart of PyCon in Asia-Pacific. It is hosted in one (1) of the following countries every year: Singapore, Malaysia, Indonesia, Philippines, Thailand, South Korea, Hong Kong, Vietnam, Japan, Taiwan, India and Bangladesh. This 2019, PyCon APAC will be hosted in the Philippines for the very first time. Learn something new, teach something new, find opportunities, build cool things, make friends, geek out! Come celebrate and enjoy this event with us! "
who it's for
Python developers, data scientists, machine learning engineers, backend engineers, DevOps and platform engineers, research scientists, engineering managers
topics
multilingual transfer learning for sentiment; explainable ML with LIME and SHAP; Python for biomedical research pipelines; Airflow and Spark analytics workflows; serverless Python on AWS Lambda; asyncio and async web frameworks; property-based testing with Hypothesis; Jupyter notebook tooling and best practices; dockerising Python applications; computer vision with TensorFlow and Kivy; building reproducible scientific clusters (MAAS, Juju); turning Flask apps async with Quart
| title | author | length |
|---|---|---|
| Opening Remarks | Matt Lebrun | |
Matt Lebrun welcomes delegates at PYCON APAC 2019 opening. | ||
| Keynote 1: PyCon APAC - Back to the Future! | Beng Keat Liew | 50m |
PyCon APAC starts in 2010 from a five-person committee that registers a non‑profit, raises 8,000+ SGD from a Django workshop to fund the first conference, and… | ||
| Keynote 2: Interviewing as a Python engineer | Jacob Kaplan-Moss | 41m |
Jacob has been a hiring manager for many years, and have hired a ton of programmers. He will talk about how to prepare for and execute a good interview. | ||
| Keynote 3: How did you know? Explaining Black Box Model Predictions in Python | Suzy Lee | 39m |
Shows concrete tools and outputs: LIME, ELI5, Skater and SHAP produce local explanations, permutation feature importances, partial dependence plots and… | ||
| Python’s role in accelerating biomedical science | Li Ting Chen | 42m |
Project sequences 4,000 single cells and faces a matrix only 2.2–2.6% filled, so code trains a random forest iterative imputation on simulated mutation trees… | ||
| Data Science and Machine Learning with Python | Argeo Tuble Alecha | 47m |
Live demo shows Python tools and exact workflows: Anaconda distribution with Jupyter notebooks, NumPy vectorized ops and broadcasting on arrays, Pandas for… | ||
| Workshop: Django Girls Workshop | Freilla Mae Espinola | |
"A beginner-friendly workshop about programming in Python and Django for women. There will be dedicated coaches who will guide you during the workshop. The… | ||
| Using Asyncio in Python 3 | Marte Soliza | 50m |
Demonstrates Asyncio and async/await on Python 3.7 with a concrete race condition: two threads incrementing and decrementing a counter over 40,000 increments… | ||
| Python's AI/ML for the Crypto-Industry | Phillip Andrew Espina | 48m |
Python gets used to prototype a 3-block blockchain in Google Colab and to call exchange APIs via CCXT wrappers; a simple trading bot is implemented and… | ||
| Property Based Testing in Python with Hypothesis | Anthony Khong | 41m |
Property based testing using Hypothesis generates hundreds of random inputs via strategies, runs them under pytest, and automatically shrinks failures to… | ||
| A Framework for Transfer Learning on Multi-lingual Text Data for Sentiment Classification | Gyenn Neil Ibo | 43m |
Claims a practical method that merges GloVe/Stanford embeddings with custom Twitter embeddings to add Filipino vocabulary, trained on ~9 million scraped tweets… | ||
| Empathy Maketh an Engineer | Narendran | 32m |
Claim: deliberate empathy measurably improves software and teams by reducing misunderstandings, fewer “WTF” moments in code and clearer expectations. Specific… | ||
| Homemade scientific clusters in Python | Taihsiang Ho | 40m |
Python orchestrates a working scientific cluster running on MAAS + KVM with orchestration via juju charms and reactive decorators, deploying master, login… | ||
| Keynote 4: Here Come The Robots - Python and Machine Learning | Tom Dyson | 49m |
Argument states machine learning becomes consumable via web APIs and Python is the lingua franca and the demoed specifics make that believable namely Microsoft… | ||
| Building Analytics Workflow using Airflow and Spark | Yohei Onishi | 32m |
Built global retail logistics analytics using Airflow + Spark and Hive, running nightly batch jobs that ingest RFID shipment and store scans into Postgres and… | ||
| Using Artificial Intelligence and Satellite Imagery to Zero In on the Philippines’ Most Vulnerable Communities | Ardie Orden, Kevin Go, Issa Tingzon | 48m |
Claims that machine learning on daytime satellite tiles plus nighttime lights proxy can reconstruct poverty at finer scales, achieving cross‑validated R² ≈… | ||
| Serverless Python on AWS Lambda using the Serverless Framework | Ted Mathew dela Cruz | 54m |
Claims a practical path to run Python on AWS Lambda using the Serverless Framework and demonstrates it end to end: uses Serverless Framework with AWS Lambda… | ||
| Automate the Boring Stuff with Slackbot | Takanori Suzuki | 39m |
A practical how-to that shows a working chatbot called PyCon JP Bot built with slackbot and concrete integrations rather than vague examples it demonstrates… | ||
| Faster Python apps with open source APM | Aravind Putrevu | 29m |
Open source Elastic APM claims to trace Python apps end to end by adding a library and shipping samples from pip install elastic-apm into an APM Server →… | ||
| Workshop: Demystifying Natural Language Processing using Python (Scikit-Learn/ Keras) | Vaibhav Srivastav | 02h 19m |
Hands-on workshop that actually builds and evaluates text models: uses Python with scikit-learn tools (count vectorizer, tf‑idf), trains a multinomial Naive… | ||
| Workshop: Introduction to Testing in Python | Aniruddha Adhikary | 02h 21m |
Workshop builds a credit card validator, fixes real bugs and adds unit tests that actually assert behavior rather than restating the topic, using pytest for… | ||
| Workshop: Graphene for Beginners | Nicole Tibay | 01h 25m |
"This will be an introductory workshop to learn GraphQL in Python using Graphene. The workshop will introduce first basic GraphQL concepts before getting the… | ||
| Workshop: Setting up Computer Vision with Kivy using Tensorflow | Jairus Jimenez | 01h 33m |
Workshop builds a small desktop app that captures a camera frame, exports a PNG, runs Google’s Inception-v3 model via TensorFlow and returns formatted… | ||
| Workshop: Learn How to Dockerize Your Python Application | Paul de Paula | 02h 28m |
Step‑by‑step recipe converts a Flask app into a Docker image using Python 2.7-slim and a small Dockerfile, produces a ~130MB image, runs Redis behind a… | ||
| Best tools for Jupyter notebooks | Dmitry Trofimov | 49m |
A quick checklist of concrete takeaways and limits: a survey of 18,000 developers finds 28% doing scientific work and 15% naming Jupyter their main tool… | ||
| Exploiting Auxiliary Information for Better Recommendation | Sun Zhu | 22m |
Claims that injecting item-side auxiliary data beats plain collaborative filtering on sparsity and cold start, with published hierarchy and KG models, code in… | ||
| Turn your Flask App into Async using Quart | Doni Rubiagatra | 35m |
Claims Quart is a superset of the Flask API so a Flask app can be migrated by swapping imports and making handlers async; demonstrates replacing Flask with… | ||
| The Use of Python in the Data World | Lai-Man Sung | 48m |
Accenture’s Data Studio claims data shifts from nightly batches to real‑time decision loops and cites 180 zettabytes by 2025 with 40% in the cloud. Concrete… | ||
| Python with NoSQL + SQL = MySQL 8.0 | Ronen Baran | 56m |
Python code uses MySQL 8.0’s X protocol to treat tables as collections of JSON data type documents, with generated id columns and CRUD operations; SQL queries… | ||
| nothing matches | ||