Optimizations for Mere Mortals

by - Dulaj Disanayaka

With current advances in Software engineering, we’re going through multiple abstraction levels when developing solutions. So it’s easy to forget that we still have limited resources. Further, code performance has real-world implications. A few milliseconds of delay can mean a significant drop in the conversion rate. So code optimization is still a very relevant topic. Optimization also means how, when, and where you allocate your (developer) time optimally to optimize your code. This is where you hear a lot of words like Premature optimizations, Micro optimizations, etc. This talk will be focusing on micro-optimizations (the root of all evil). But we’re specifically talking about some good habits you and your team can develop so you will write non-ugly code. The main idea of doing this is that, without effort, you can write good enough code, so you won’t have to worry about micro-optimizations in the future.

Create a (Stellar!) Profile Summarizer WebApp using OpenAI API, PDFPlumber, and Streamlit

by - Nabanita Roy

Generative AI has taken over 2023. This talk is centered around how to access OpenAI's generative AI models (GPT3 and ChatGPT) and use them for this case of generating a profile summary based on the information you provide. It is surprising how the models respond to different elements in the prompt. Along with this, I will demonstrate how to build a basic Streamlit WebApp and parse PDFs using PDFPlumber to feed information to your webapp.

Big Data for Large Scale Machine Learning with Apache Spark

by - Sahan Bulathwela

The amount of actionable data that is available for organisations has exploded in the last two decades. This explosion has led to a brand new opportunity in terms of harnessing this data to extract value to business. In this talk, the speaker describes how Apache Spark, a big data technology that has a Python programming interface, can be utilised to build big data products. The Speaker will lay the foundations to the topic by defining what big data is and how big data is created in the real world. Then he explains the architecture of Apache Spark and how Spark can be used to leverage distributed computing that is pivotal when working with giga/tera and peta-byte scale data. The basic API of PySpark, the python interface of Apache Spark will be presented with coding examples to demonstrate how to develop big data applications. Finally, the talk will conclude by giving a glimpse into Spark ML, the distributed machine learning package shipped with Apache Spark.

What happens when you import a module

by - Reuven Lerner

Modules are a key feature of Python, allowing us to easily reuse our own code and take advantage of publicly available modules from PyPI. It's a rare program that doesn't include at least one ""import"" statement. But what actually happens when we import a module? How does Python find our file? How does it decide whether it should even try to find our module? And after it finds our module file, how does Python load it into memory, assigning to its attributes?

In this talk, I'll walk you through what happens when you ""import"" a module into Python. The mechanism is surprisingly complex, in no small part because it has to take so many possibilities into consideration. We'll talk about finders and loaders, and about the many ways in which you can customize the module-loading mechanism if you find a need to do so.

If you've ever imported a module, then this talk will pull back the curtain a bit, helping you to understand what's happening under the hood.

How descriptors work

by - Rodrigo Girão Serrão

Descriptors are one of those things that many don't even bother to learn because they look confusing. Thankfully for you, I figured out how descriptors work 20 minutes ago, so there is no one better prepared to teach you descriptors than someone who _just_ learned how they work. In this talk I will provide an example that shows how descriptors work and that will also motivate why they are useful and needed. By the time we are done, you will be able to implement descriptors in your code, use them for practical purposes and, most importantly, you will be able to deliver a talk just like mine! The talk will be easier to follow if you are familiar with how properties work (via the `@property` decorator).

## Outline

The outline of the talk is as follows:
- using properties to implement getters for private attributes or attributes that are computed on the fly;
- using properties to implement setters for those same attributes;
- defining a bunch of similar properties;
- using descriptors to abstract away the pattern that the properties follow;
- descriptor `__get__`;
- descriptor `__set__`; and
- descriptor `__set_name__`.

The talk will start by going over the basics of properties, but it is does not provide a comprehensive introduction to Python properties. Rather, it is meant as a refresher for someone who knows how to implement and use a property."

Discover the full potential of python match case

by - Chamath Ranasinghe

Have you ever wondered why there is no switch-case statement in python and googled for it? If you did it after the python 3.10 release you might probably get pointed towards “match-case” in python. I think it is one of the most underrated features in python because many of us try to use it as an alternative to simple switch-case in other languages. But it has more potential than that whilst you can't expect any real performance benefits from replacing a few if-elif statements. In this talk we will discuss how match-case actually works and its advanced usages. After this talk you will be able to effectively use “match-case” in your programs.

Overcoming Concurrency Issues in Web Applications

by - Haki Benita

"Concurrency in web applications is so easy to get wrong, and so hard to identify and debug when it comes to bite you. In this talk I'm going to present common concurrency issues and suggest ways to identify and prevent them!

Description

In this talk I'll present common issues, and different approaches for dealing with concurrency:
Why is it better to ask for forgiveness instead of permission when dealing with concurrency
What is the TOCTOU (time-of-check-time-of-use) problem, how it can happen and how to address it
How database locking can help with concurrency, and how to avoid it!
How and when to rely on database features to maintain correctness

Adhering to DRY principle with Django templates

by - Indika Rajapaksha

Django provides a powerful template system encoraging the DRY principle similar to its core modules. Starting with basic tags, let's see how we can reduce the code base significantly using template blocks and through the interitance.

Optimizing Web Performance: Introduction to Caching in Python

by - Manas Najmuddeen

This introductory talk dives into caching principles and their use in Python web development. We'll explore the benefits of caching, performance enhancement strategies, and its integration in Python frameworks like Flask and Django. The talk will underscore caching's role and best practices, and highlight common pitfalls. Attendees will gain insights on how caching optimizes Python web applications for improved performance and user experience.

Deploying Machine Learning Models into Production

by - Manik Fernando

In most AI powered projects, teams tend to put a lot of effort into the model training phase in order to obtain a target metric. But this is only part of the battle.
This talk aims to serve as an introduction to the model deployment process after a successful training phase.
We will be exploring the different modes of inference, model lifecycle management, monitoring as well as the challenges involved.

Scaling Radiology to Serve a Billion People using Python and AI

by - Bargava Subramanian

Radiology is a critical field in modern medicine, but it faces a major challenge: the growing demand for diagnostic imaging services is outpacing the availability of trained radiologists. In this talk, we will explore how Python and AI help scale radiology to meet the needs of a rapidly growing population. The talk will discuss how to use Python to develop efficient and effective algorithms to analyze medical images, and how AI, both computer vision and natural language processing, is used to automate and optimize radiology workflows. The speaker will also show how to build quick internal apps using Python to improve the accuracy and speed of diagnostic imaging.

- Introduction to the challenges and opportunities in radiology
- Overview of Python and AI in radiology
- Developing Python algorithms for analyzing medical images
- Using AI to automate and optimize radiology workflows
- Building internal tools using Python
- Real-world examples
- Q&A

Automate Traffic Simulation Models with Python

by - Thenuwan Jayasinghe

Modeling urban transport networks helps to better evaluate the performance of the network and propose suitable traffic control measures. Developing accurate transport models requires a large amount of time as i) it requires multiple data inputs in the model creation process, ii) proper calibration of model parameters to match the ground truth. This session discuss how Python programming language can be used to automate i) model creation, ii) model calibration (using stochastic approximation algorithms), iii) scenario development, so that the time required for model development can be minimized while maintaining a higher level of accuracy in the model. Real world applications with SUMO microscopic simulator and PTV Visum macroscopic simulator will be discussed in this session.