PyCon SE 2019

Annual conference opening by Anna K. Lindegren.

As we progress further into the Information Age, the number of factors involved in making important business decisions increases exponentially. We will discuss the benefits of operationalizing Python models to better inform the increasingly complicated choices that need to be made in an increasingly complicated world, as well as highlight examples that showcase the ubiquity of this approach. Speaker: Shammamah Hossain. Since June 2018, Shammamah has been working at Plotly as the main engineer for the Dash DAQ and Dash Bio libraries.

Extracting insights from a complex machine learning model is not easy hence for many people machine learning models are in a sense black box. This is a problem especially in high stake sectors like banking and healthcare. In this talk we will discuss how we can increase transparency, auditability, and stability of the model using valuable insights we can get from SHAP and explain reasoning behind individual predictions and how this can be aggregated into powerful model-level insights. We will also see the code to calculate SHAP values. Audience level: Intermediate Speaker: Ravi Singh. Data Scientist at HBO Europe developing predictive models and influencing and driving the way the marketing team consume data and insights through highly usable and visual data analysis products.

What is causal inference, and why should data scientists know? × With an explosion of computation power and modern algorithms more and more people are interested in AI, Analytics and Data Science. The Python ecosystem has been one of the most important driver for developing new tools and Python holds the power of modern analytics, but knowing the tools is not enough. Drawing conclusions from data is easy; getting the right conclusions is hard. Causal Inference is the art of drawing robust conclusions from nonexperimental data. This session will be an introduction to the field. Audience level: Novice Speaker: Ludvig Hult

How adjust your AI solution to the new domain? How retrain your model on new industry-specific data? How to increase labeled dataset for minimum cost? Do oracles exist? We will look at these problems from the data protection point of view and bring possible solutions. We will talk about methods, which discuss how to adapt NLP and CV solutions to any type of industry. Speaker: Tetiana Kodliuk

The talk is about sharing the journey & learnings from building a world class AI function that builds scalable software for the entire H&M group. In the talk we will go through the tech stack behind it and some of the key enablers for us to scale and drive value from AI. Speaker: Errol Koolmeister. Errol is the head of AI tech & architecture at H&M group and acting head of Data Science. In his role he is responsible for overseeing and coordinating the AI projects run at H&M. He has spent the last 10+ years in different industries such as banking, telecom and consulting and his main focus has during this time been setting up and scaling AI and big data projects.

Writing highly scalable and provenanceable data pipelines with Kubernetes and Python In this talk we are gonna explore launching and maintaining highly scalable data pipelines using Kubernetes. We are gonna go through the process of setting up a Pachyderm cluster and deploying Python-based data processing workloads. This setup enables teams to develop and maintain very robust data pipelines, with the benefits of autoscaling clusters and quick code iteration. Audience level: Advanced Speaker: Guilherme Caminha, Software Engineer from Brazil. His interests include Scientific / High Performance Computing, Backend Development and Machine Learning.

Python is huge in finance, from banks to hedge-funds to day-traders; it is often the go-to tool to get things done. This talk dives into 'why?'. First looking at several areas where Python is big, what kind of work is involved, and the challenges for developers? Then at what libraries/language aspects make Python particularly suited. Pandas gets particular attention as it is incredibly useful and can make you super productive. We finish up with a demo of how you can use Monte Carlo techniques to build a mini stock market simulation and test out your own trading ideas. After the talk, you should have a better feel for what it's like working with Python in finance and also a good set of starting point if you want to start experimenting. Audience level: Intermediate Speaker: Daniel Roos, with 15+ years of experience building Python/C++ financial systems for global banks and hedge funds. He has recently co-founded Njorda, a retail-focused fintech based in Stockholm.

Generative art leverages computing power to produce elegant artwork, by skillfully controlling the location and magnitude of randomness in color, shape and position. Contemporary artists are more than ever using such principles in their creative workflow, to produce image, video and sound. This talk will give you an overview of the possibilities of using code in an expressive way, and inspire you to start drawing with Python. Audience level: Advanced Speaker: Half Scheidl works as a team lead and project manager in H&M Advanced Analytics, building world-class data platforms and machine learning services with cloud services and software engineering. He is an aspiring digital artist and guest lecturer at Beckmans Designhögskola in Stockholm, engaged in various activities related to digital art. His artwork combines code and technology to create illustrations, animations and interactive installations. Half is the organizer of the Creative Coding Stockholm Meetup group, and has recently exhibited in the Winter Festival at Harpa concert hall in Reykjavik.

Python has become the most widely used programming language. From time to time, employed professionals consider going on their own and become a freelancer. This talk will cover the most demanded Python based services for outsourcing. Anna will also talk about the freelancer mindset together with the legal and economical aspect of being self-employed in Sweden. After the talk, you will be able to see the full picture of freelancing and decide for yourself, if it is for you. Audience level: Novice Speaker: Anna Kazakova Lindegren, Data Scientist, IBM. Anna is a data scientist with a background in accounting and auditing. Before switching to data science in 2017 she had been providing these services to different IT consulting companies and self-employed freelancers for several years. Among her clients were Praqma AB, Tedx Stockholm, A59 and many others.

Stories From Real World Vision Projects How do you implement an application that can pick out the interesting parts of a soccer game? What do you do when you don't have enough data, or when the data is too specific? How do you know that you are actually solving the problem, or even the right problem? Writing a neural network in Python using Keras is very straight forward, you can do it in 20 lines or less, but is that all there is? Welcome to a journey through some real world problems and the thinking when working through them. Speaker: Tess Ferrandez. Tess is a software engineer/data scientist working at Microsoft. She works primarily on computer vision projects and more specifically lately on video action detection projects with some of the largest retailers and media companies in Europe and US. Read her interview here: http://www.pycon.se/blog.html#blog-tess

The goal of this talk is to empower people with over a dozen optimisation techniques which may be effectively used in a wide variety of situations, even beyond Python. I will present best practices, typical pitfalls and common tools, but the main focus will be on a practical approach. I will showcase a small problem and a naive solution, just a few lines of Python, so that it's easily understood. Iteratively I will apply each optimisation, explain the reasoning behind it and note how execution time is reduced. By the end of the talk, one will see how the code evolved from something that would take bigger than 1 billion years to compute to smaller than 1 second on a regular laptop. I will also compare running times between Python, PyPy and C++ implementations (one being just a few milliseconds), and show how the techniques may achieve vastly different speedups from the python versions. Audience level: Intermediate Speaker: Isaac Bernat (github.com/isaacbernat), back-end engineer at Ivbar, LOGEX Group

Developing Python applications is handy and rapid, but its performance is always concerned, especially on the CPU bound problems. We will go through the common tricks and tips to archive the best performance on the Python level. Then the two reputable libraries, Cython and Pybind11, will be visited to archive the compiled language performance and compared with their implementation, flexibility and performance. Audience level: Intermediate Speaker: Gavin Chan, quantitative developer in AXA Investment Manager Chorus Ltd with 7+ years of experience in software development and finance industry.

The talk is particularly useful for developers of Python applications which does heavy computation, with or without NumPy - for data science, research etc. Python extensions allows for creation of high-performance applications, which can compete with C or C++ based ones. There are more than one framework to achieve this (for example, Cython, Pythran and Numba) with similar syntaxes but different underlying implementations. The talk surveys the state of the art of creating extensions and introduces Transonic (https://transonic.readthedocs.io). Transonic is a pure-Python package acting as a unifying front-end for writing extensions with the aim to enhance the developer experience. Audience level: Intermediate Speaker: Ashwin Vishnu Mohanan, Ph.D. in Engineering Mechanics from KTH and post-doctoral researcher at Stockholm University. 5+ years of experience as a research software developer and contributor to various open-source projects.

Asyncio is a fairly new tool and no often used in production. In this talk we will be aiming for use-cases and examples so people can get more confidence moving away from the synchronous world. The talk aims to solve the problem of uncertainty when getting started with asynchronous programming in Python. After the talk the audience will be equipped with new tools they can look up at home to get started with asynchronous web development in Python, also, hopefully, they will have some extra confidence in the area. Slides from talk: https://github.com/akoskaaa/pycon-se-2019 Audience level: Intermediate Speaker: Ákos Hochrein, software engineer and book enthusiast. He worked at various companies both on the frontend and the backend to deliver highly available and scalable solutions to their customers using the power of Python for the past 10 years. In his free time, he likes to read about psychology and dystopias while exploring the beer culture of Berlin.

Test Fast, Fix More – Property based in Python testing with Hypothesis Did you ever miss that corner case bug? Maybe it was a negative integer, strange timezone conversion behaviour, off by one error or something entirely else. These subtle bugs are often hard to catch and are easily missed in test cases. You like me have probably ran into plenty of code utilising only happy path testing, only to later discover subtle bugs which are easily fixed once pointed out. This is where property based testing comes into the picture. In this talk I will focus on a wonderful Python library called Hypothesis but the concepts apply to other languages as well. Hypethesis is based on the same concept as the famous QuickCheck library for Haskell, which in turn have been ported a large number of languages. Hypothesis uses a wide range of input to find edge cases that you could otherwise easily miss, once it finds these cases it narrows down the input to the minimal breaking example to provide failures which are easier to understand. Audience level: Intermediate Speaker: Alexander Hultnér

PyMacaron is a microservice framework integrating Flask with swagger specifications to spawn REST apis with minimal code overhead. It comes with a deployment pipeline towards docker and amazon Beanstalk, support asynchronous tasks out of the box, and plays well with DynamoDB. PyMacaron is live and powering the backend of a couple of Swedish startups, including ksting.com and bazardelux.com. Audience level: Intermediate Speaker: Erwan Lemonnier

What is mutation testing? How does it work in practice? What is it like to actually do it? These questions will be answered! Audience level: Intermediate Speaker: Anders Hovmöller

The shape of the tool determines the outcome of the result. A story about developing a volumetric 3D modeling and animation tool for creating virtual and physical sculptures using VTK, Numpy and PyQt told through the tool itself. Audience level: Intermediate Speaker: Victor Nyberg, contemporary artist and software developer.

PySpark is a great tool for doing big data ETL pipeline. While designing a big data pipeline, which is easy to maintain with a holistic view, simple to spot bottleneck is difficult. Not to say enable analytics on ETL pipelines. Rockie Yang will share his experiences on build effective ETL pipeline with PySpark. Audience level: Intermediate Speaker: Rockie Yang

When we're learning something new - even something as friendly as Python - it can be difficult to make details stick long-term. This talk will give you some tips to help you learn Python - or anything else - more effectively. Although Python beginners will benefit the most from applying these concepts to Python, this talk is for anyone who wants to hack into their potential to learn more efficiently. Audience level: Novice Speaker: Jessica Shortz. After spending years working as an attorney, Jessica has been working toward a career change to programming over the past two years. She's still searching for the perfect Python IDE, so feel free to recommend your favorite to her if you see her at the convention!