Are your debugging skills limited to "print", or do you sometimes think there must be a better way to figure out what's going on? I will show 10 ways to debug Python code, and share tips and tricks for effective debugging.

Local development of python code inside a docker container is surprisingly broken. In this talk I will walk you through the proper setup of a local python development environment using docker.

Bayesian Modelling has several advantages such as the handling of uncertainty. While the advantages are well known, implementing a Bayesian model can be a bit more involved and some care needs to be taken to check whether the model converged.

Medieval people invented one of the first domain specific languages to describe how to paint Coats of Arms. We'll learn how to write our own parse grammar to parse this language, and then look at the parse grammar of Python itself!

JupyterLab can be extended via third-party extensions written by developers from the Jupyter community.

This is a tour of 20 of these extensions, in 20 minutes. Demos included!

Datalore supports intentions – code suggestions based on what you’ve just written. They cover a wide range of situations from generating code to warnings and optimization suggestions.

This talk is not about how to use pytest. pytest is a good project to explore metaprogramming techniques like introspection and code as data in the context of solving real world problems.

In this case: implementing a test framework.

In most of the systems, collecting data is not always free. I will talk about an approach for a matrix completion problem that learns a distribution of data where information is incomplete or collecting it has a cost.

Now that you finally have your Machine Learning model trained, what’s the next step for moving to production?
Orchestrating, scheduling and monitoring ML inference pipelines is a big challenge.
Airflow can be your ally for handling this complexity.

In the keynote I will present our Algo.Rules, 9 rules for the design of algorithmic systems and address the questions “What standards of quality should algorithms be held to?” and “How can we make sure that these standards are actually being implemented?”

Python concurrency and parallelism concepts like Multiprocessing, Multithreading, Coroutine, Asynchronous
I/O will be explained. We will learn to write simpler code with improved response time and throughput.

Developing ensemble model with hundreds of features? And getting stuck for months trying to deploy the model and fighting with data inconsistency and bugs? This talk will introduce the way to build the development process with deployment in mind.

There is a strong need in many AI applications to state the certainty about their predictions. This talk elaborates on different ways to perform uncertainty quantification in deep learning and classical methods.

Careful feature engineering and selection can be just as important as choosing the right ML model & hyperparameters. I will present several options for automating the feature engineering and selection process with a focus on the autofeat library.

Feature engineering is crucial for supervised learning (SL), but labor-intensive. Automating this task is less established, compared to other steps of SL. We therefore present and compare approached and open-source libraries for automating this task.

Everyday there is a new package or algorithm to use- it can be hard to determine what is useful and what is only hype. The speakers offer a practical roadmap and checklist to help you cut through the hype and focus on developing useful ML products.

Focus on features, not paradigms. This new approach to the study of programming languages offers practical advice for programmers learning a new language, adopting coding idioms, and choosing suitable design patterns.

In this talk I want to demonstrate how to use OpenCV to implement basic animal movement tracking use cases. And everything without any fancy machine learning or neuronal networks ;)

In this talk I will present the architecture of our simulation (written in Python) which allows us to simulate hours of real-life in only minutes of simulation. I will describe challenges we encountered and how we handled them.

As hard as we try to write good code, there will always be cases in which we wish we had designed a different function signature, chosen another attribute name, ... But you can't change it anymore because it's public API - or can you?

With increasing focus on Machine Learning systems in almost every business it is important, to build a great pipeline to train, test and deploy your models. In this session we will show a way to do that with Jupyter and Azure

Get well, soon, Valentin!

We'll explain how to do GPU-Accelerated numerical computing from Python using the Numba Python compiler in combination with the CuPy GPU array library.

Cancelled - get well soon, Felicia!

The community of Julia has been growing and the much anticipated 1.0 release of Julia is out since last summer.
Targeted to Julia beginners and Python users, we will especially highlight Julia's benefits and its major differences to Python.

Get well soon, Dmitry!

This is an EdgeDB talk. EdgeDB is a new database from Python core developer and uvloop and asyncpg author Yuri Selivanov. It's nor NoSQL nor it a NewSQL. Looks very promising!

Introduction to Python hardware description libraries, and how they are being used to design modern silicon, including open-source RISC-V CPUs.

Good code comments are important for software maintenance. This talk
goes beyond the common wisdom you find in most books and online and
explains when this common wisdom falls short.

Our goal is to create a simple yet interactive showcase for computer vision using a Python notebook. In a trade fair setup, we want to learn new object classes quickly using very few training examples. Thus, we rely on pretrained neural networks for

As a scikit-learn core developer, I'd give an update on recent changes, current affairs, and the roadmap of the package and the community packages included in scikit-learn-contrib. I'd also briefly talk about how new method proposals are evaluated.

Interactive web pages and visualizations with no JavaScript? What could go wrong? What you can, can't, should and probably shouldn't do with plotly/Dash.

Artificial Intelligence need to be better understood in enterprises. Close the communications gap between engineers and management. Making data litteracy happen in your organisation.

Federated Learning is a technology to train machine learning models and run data analytics on decentralized data. This tutorial will demonstrate step-by-step how to train largescale TensorFlow models and custom computations in federated environments.

This tutorial provides a general introduction to Deep Learning using PyTorch with specific focus on challenges and solutions for Healthcare and Computational Biology.

We present a novel method for detecting solar panels and its geometry on aerial imagery. The goal is to know the exact locations, dimensions and potential of every solar installation in Switzerland.

This 30-minute talk will give you an overview about project management, success factors and specialties within enterprises. This will be a guide how you promote internal projects and bring them to success within a highly "political" environment.

Containers have revolutionized the way we build and ship software. This talk is aimed at newcomers and people with limited experience with Docker and will cover the basics of Docker and how it can be used alongside Python.

How might we make social media safer and more inclusive? Tackling hate speech online is not easy, especially if it’s in a language less circulated. This talk describes detecting hate speech in Romanian from dataset creation to hate speech model.

Explore the benefits of taking a functional approach when writing Python in Jupyter notebooks: reduce errors caused by out-of-order execution and hidden state while producing more readable code.

OctoPrint is an open source web interface for 3D printers and deployed world wide on a large variety of devices. In this talk I explain some of the challenges in developing and maintaining such a piece of end user facing software in Python.

Usually, uncertainties of Machine Learning predictions are just regarded as a sign of poor prediction accuracy or as a consequence of lacking input features. This talk illustrates how modeling uncertainties can improve ML based decisions.

In this talk, we will explore how the weight-sharing property of the convolutional layer can be generalised to achieve equivariance towards transformations beyond just translation, how to implement this, and the results on real-world data.

After three years of utilizing event sourcing in Growbots, I want to introduce to you its basics and share our experience acquired over that time - including complexity involved - so you can better assess whether it's a tool wo

Get well soon Marie-Louise!

As our world becomes more and more data-driven, journalists are equipping themselves with data science and programming skills to turn numbers into headlines.

In this tutorial, we are going to deep dive into algorithmic fairness, from metrics and definitions to practical case studies, including bias audits using Aequitas (http://github.com/dssg/aequitas) in real policy problems where AI is being used.

R and Python are partially overlapping but different as a whole: community and language. Still, comparing them in their common fields such as data wrangling and visualisation, useRs and Pythonistas will deepen mutual understanding.

This talk is meant to bring awareness about the need of diversity from theoretical to practical day by day actions. Diversity not only as social justice, but to reclaim knowledge proficiency, critical perspective and new ideas.

The aim of this talk is to introduce the notion of Gaussian process and describe
how to use it to solve regressions problems and time series forecasting.

This talk is an attempt at explaining the power of the Gaussian[tm] by stepping up the ladder from Naive Bayes to Mixtures to Neural Mixtures to Gaussian Processes.

This session will be an exposition of data wrangling with pandas and machine learning with scikit-learn for Python Programmers. This hands-on workshop will cover a classification project, from importing the data to evaluating model performance.

In this review, we'll look into frameworks that will help Python developer start working with FPGA without prior knowledge of Verilog or VHDL.

This tutorial describes the intuition behind Hidden Markov Models, with less mathematical formulas and with an application on Music Analytics - Chord Recognition

PyTorch makes developing, training and debugging deep neural networks convenient. Learn how to export your trained model using its just-in-time (JIT) compiler to hide your network architecture, minimize code dependencies and use it in the C++ API.

MicroPython is a lean and efficient implementation of the Python 3 programming language, optimised to run on microcontrollers and in constrained environments. One of these environments is on a spacecraft.

Being able to correctly estimate a competitors' skill is a crucial question in sport forecasting and matchmaking. This talk will provide an overview of the three most common algorithms for this task: Elo, Glicko2 and Trueskill.

Everybody is doing colorful charts with Python libraries such as matplotlib and bokeh but most people never change the basic configuration. This talk will teach you the basics of color theory to help you choose the right colors of your charts.

In this talk Sander explains how you can write unit and integration tests that need a lot of data. As an example Sander shows how to test code of a warehouse management system (WMS). This is not about big-data, though.

Complex machine learning models make better predictions, but at the cost of turning into an unexplainable black-box model. In this talk, we'll look into a framework that allows us to explain why a model makes a specific prediction.

We'll learn how to get started with developing automated tests in Python with the pytest test framework.

People refer Python's Global Interpreter Lock as main bottleneck for their performance critical applications. But how can you test if your application really suffer from the GIL?

Panel about freelancing and the experience of moving from academia to industry, challenges, opportunities and where to start.

Julia is a new Language, that is fast, high level, dynamic and optimized for Data Science. But due to its young age, it might not be for everyone yet. Learn about Julia's strengths and how you can integrate it in your Python workflow!

Efficient data storage is an integral part of successful data applications. Cloud object stores prove to be an efficient choice but come with downsides when storing structured, tabular data. There is a way out, though.

Kubernetes (K8s) is an open-source system for automating deployment, scaling, and management of containerized applications.

During this training you will learn how to use it and how it can help as a Python developer.

The talk addresses how law and ethics can provide a framework for the “machines on the rise” that ensures fairness and societal acceptance while not interfering (too much) with the drive behind the advancement of a powerful technology.

The fun part about data science is that no two people really agree on the definition of the data scientist role. So how does the role of product manager in data science look like?

Bayesian models offer greater theoretical advantages compared to non-probabilistic methods, and also allow for more flexible model design. But how can one leverage these theoretical advantages to build a successful data science product?

This talk covers the theoretical background behind two common loss functions, mean squared error and cross entropy, including why they are used for machine learning at all, and what limitations you should keep in mind.

This talk is about the coupling of a digital twin model and a machine learning predictive maintenance algorithm in order to be able to detect anomalies in the operation of a not well-known hardware system.

Creating graphics that convey the desired message, are easily interpretable, but also beautiful can be a daunting task. This talk will demonstrate how to use The Grammar of Graphics framework to conceptualize the elements of any graphic, in Python.

Machine learning requires experimenting with datasets, data preparation steps, and algorithms. Deploy models to a production system and retrain it on new data. MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

Mock is an easily abused tool. In perverse cases, it increases technical debt and prevents refactoring. This talk describes the history of mocking, alternatives, anti-patterns, and the connection to clean architecture.

Monitoring is a key aspect for any business. It enables us to find and be notified about our application's problems way ahead our customer notices it, which enables us to keep our businesses running and making customers happy.

Keyboards, the main way we interact with computers, have remained unchanged for a century, despite being free from mechanical necessity. Alternatives made possible by recent technology can optimize the way we input text and interact with our devices.

The acceptance of PEP 581, by Python steering council means that another big workflow change is impending. Let's hear about some of the proposed plans on improving CPython's workflow, and learn how you can help and take part in this process.

Poetry is “Python packaging and dependency management made easy.”It not only packages up your libraries easily but also isolates environments and resolves dependencies — all with an intuitive command line interface.

Quickly turn your existing analyses built on the PyData stack into shareable, standalone apps and dashboards using the new open-source Panel library without learning the details of web development.

Despite all of python's strengths, parallel programming tends to not be one of them. Enter Go, a language that has parallel programming at its core. In this tutorial we will build an API pipeline service in Go, that calls several APIs in parallel.

This is reserved for a James Powell in-promptu talk, stay tuned! This is reserved for a James Powell in-promptu talk, stay tuned!

How many times have you developed a model or a data application and tested it locally or in a staging environment just to find out that it breaks in production? This is a common issue faced by thousands of data scientists around the globe. As the wor

Privacy is something we all care about, but when it is time to put our principles into application, it is not so trivial, especially when working with text. This talk aims at presenting a few options to handle privacy when dealing with text.

Learn how easy it is to apply software engineering principles to your data science and data engineering code. Expect an overview of Kedro, a library that implements best practices for data pipelines with an eye towards productionizing ML models.

In this talk you will learn how to level up your skills, and develop your your career using on the job opportunities, as well as open source contributions

While very successful, Python's peculiarly missing in some spaces like mobile devices, client-side Web, or gaming. Should we do something about it? How could we go about changing that?

Panel about Python, core-devs, development, challenges, community and the future! And: how can become a part of it! Join the discussion!

OSINT, the discipline of gathering intelligence from open sources, is critical for state & citizen interest but has lacked modernisation. We present a socio-technical maturity model using Python to update legacy OSINT for disinformation investigation

Frameworks for quantum computing are a new way to use Python for cutting-edge science, and to plan for future applications of this new technology. This session will serve as an introduction to quantum computing as a whole, and also to Qiskit, the most well-developed and well-used quantum Python framework.

And to make sure I don't get too complex, we'll do it all on a microcontroller!

Experiences and lessons learned from tackling extremely demanding code. How to bring order to mismanaged code and elevate the code base to a standard that's acceptable in today's tech environment.

By some measures, Open Source is a wildly successful and crucial part of many areas of modern technology. However, the ’sustainability crisis’ and the age of cloud computing have threatened its core mechanisms. Peter will present some alternative ways of looking at this crucial moment in the evolution of the open source movement, and suggest some ways to think about the future of open source.

Not so long ago, I started an open source project - PicknMIx. It feels like running a start up if you are serious about it. Want to know my story? Want to check if you can do it as well? I will tell you. (Stickers not guaranteed though)

This talk presents a Python implementation of the Longstaff-Schwartz algorithm for financial exercise option valuation. The technical problems of optimal exercise decisions and exercise option valuation are demonstrated using code examples.

In this talk, we'll discuss the advantages and disadvantages to a static type system, as well as recent efforts to introduce static typing to Python via optional "type hints" and various tools to aid in adding types to Python code.

Over the past few years, GraphQL has gained much traction, especially in the JavaScript world. Python is getting on board this trend with new interesting libraries. In this talk, we will see how Strawberry makes uses of dataclasses and type hints to

Data Science for Social Good Berlin (DSSG) brings together data scientist volunteers and nonprofit organisations that need support in tackling various data challenges. In this talk we will tell you how data science can be used for social good.

Have you ever wanted more control over what you do or don't hear? This talk explores the potential of Python, deep learning, and open databases to bring you towards that goal, without needing expensive licenses or software.

Female-identifying people are being attacked and silenced online. Social media platforms act as neutral bodies and law enforcement can’t stop the abuse. When you can’t trust that your safety will be protected online, what can you do? We say, Opt Out.

This talk discusses time series anomaly detection methods for predictive maintenance of machines in bottling plants and their implemention on AWS edge devices.

This talk focuses on a Bayesian approach of advanced time-series forecasting in the case of small data. We describe the stages of modelling from simple smoothing to advanced forecasting by applying Gaussian Processes and State Space Models.

There is now a wealth of tools that support data science best practices (e.g. tracking experiments, versioning data). Let’s take a look at which tools are available and which ones might be right for your project.

We present Phoenix, a modernization of a legacy, German rule-based system based on Tesseract and SpaCy, and uses AI Fairness 360 to build tunable, Bias-Minimizing AI solutions to offer payment incentives to debtors.

Building a sensor platform that is flexible and intuitive is hard! This talk takes you through a journey how a sensor platform was developed to create a sensor farm for the purpose of capturing and acquiring data to be used in AI systems for Samsung.

We will cover several techniques to expose weaknesses and robustify neural network models for computer vision, from basic precautions to more advanced adversarial training.

As a practical example, this tutorial uses machine learning models to predict where to best place the apple in Snake. By using datasets that contain different plays we can obtain different game experiences or models that can adapt to the style of a player.

Are you versioning your Machine Learning project as you would do in a traditional software project? How are you keeping track of changes in your datasets?

In this talk you will learn how to visualize graph networks in Python, using networkx, traitlets, ipywidgets and plotly. The resulting plot will be fully interactive, which makes it easy to filter edges, find nodes by their name and update the graph's layout.

Discover your individual approach towards more positive social impact by conducting experiments. I'll show you how! I’ll also share my learnings from doing such experiments over the last 15 years.

This talk is going to be about Swagger Specs and their changes. We're going to examine what Backward Incompatible changes are and how you can deal with them.
The talk will also introduce you to a tool that will help you on having safer changes.

Web is broken. We learned the hard way. Developers tend to hack, hacks tend to break the web. In this talk, I share what we learned how websites don't obey the protocols and how developers had caused the web became a chaotic medium.

In few months, there will be the release of Python 3.8! As a core dev I would like to show you the new features of this version.

This talk explains how linguistics describes language - via phonetics-phonology, morphology, syntax, semantics and pragmatics. We will combine linguistic concepts with models through examples for NLP newbies.

Reinforcement learning is the closest thing to a general AI system that we have. When it works, that is. The problem is, it often doesn’t. In this talk we will discuss the difficulties of RL as well as what to look for in the future.

Language models like BERT can capture general language knowledge and transfer it to new data and tasks. However, applying a pre-trained BERT to non-English text has limitations. Is training from scratch a good (and feasible) way to overcome them?

Decorators are really useful. Using them is simple. Writing your own is bit more involved. Learn in this hands-on workshop how to write decorators for many different purposes. The emphasis is on best practices and practical examples.

About people with first names, middle names, last names, one-word names, multiple names, and changing names, about names with characters beyond ASCII, and about using Python to handle them correctly, because names of people cannot be invalid.

The pytest tool presents a rapid and simple way to write tests for your Python code. This training gives an introduction with exercises to some distinguishing features.

This talk is about the open source package skorch, a wrapper library that allows you to combine the best of sklearn and PyTorch. It covers when it makes sense to use skorch and highlights interesting features.

In this we talk present how to write Python extensions in Rust, and discusse advantages and limitation of such approach. We then illustrate this approach on the vtext project, that aims to be a high-performance library for text processing.

This talk gives an introduction to Apache Airflow, that facilitates workflow automation and scheduling. You will learn about the core concepts in Airflow and how they fit together to form a data pipeline.