Conference Talks on Applied Python
Python is a very popular general purpose programming language. It is used everywhere, in many diverse domains, including science and research, artificial intelligence / machine learning, many industrial and business aspects, and many other entity or personal purposes.
In the latest popularity rankings, Python positions at the top, either the first or second champion (according to RedMonk and IEEE Spectrum data). One of the strengths of Python lies in the community / ecosystem. There are many packages built for different usage, so that whatever we want to do, there might be likely a package we can start with.
We curate more than 100 awesome conference talks directly or indirectly related to Python, especially for the domains addressed by the three conference groups (Linux Foundation, Orelly Media, NANOG).
Talks in 2020
| Migrating AI-infused chat to Kubernetes | Steven Jones (IBM), Nicholas Fong (IBM) | O’Reilly Software Architecture Conference 2020 |
| Technical debt: A master class | r0ml Lefkowitz (Retired) | O’Reilly Software Architecture Conference 2020 |
| Network understanding for all - open sourcing the network model and unlocking the value of understanding the wide area network | Tim Fiola | NANOG78 2020 |
Talks in 2019
| Releasing improved serverless functions with confidence | Jochem Schulenklopper (Xebia), Gero Vermaas (Xebia) | O’Reilly Software Architecture Conference 2019 |
| A novel solution for a data augmentation and bias problem in NLP using TensorFlow | KC Tung (Microsoft) | O’Reilly TensorFlow World 2019 |
| Anomaly detection using deep learning to measure the quality of large datasets | Sridhar Alla (BlueWhale) | O’Reilly Artificial Intelligence Conference 2019 |
| Deploying machine learning models on the edge | Yan Zhang (Microsoft), Mathew Salvaris (Microsoft) | O’Reilly Artificial Intelligence Conference 2019 |
| Machine learning challenges at LinkedIn: Spark, TensorFlow, and beyond | Zhe Zhang (LinkedIn) | O’Reilly Artificial Intelligence Conference 2019 |
| Building an AI platform: Key principles and lessons learned | Moty Fania (Intel) | Strata Data Conference 2019 |
| Data science and the business of Major League Baseball | Aaron Owen (Major League Baseball), Matthew Horton (Major League Baseball), Josh Hamilton (Major League Baseball) | Strata Data Conference 2019 |
| Deep learning methods for natural language processing | Garrett Hoffman (StockTwits) | Strata Data Conference 2019 |
| Scalable anomaly detection with Spark and SOS | Jeroen Janssens (Data Science Workshops) | Strata Data Conference 2019 |
| Your easy move to serverless computing and radically simplified data processing | Gil Vernik (IBM) | Strata Data Conference 2019 |
| Introducing Kubeflow (with special guests TensorFlow and Apache Spark) | Holden Karau (Independent) | O’Reilly Artificial Intelligence Conference 2019 |
| The OS for AI: How serverless computing enables the next gen of machine learning | Jonathan Peck (GitHub) | O’Reilly Artificial Intelligence Conference 2019 |
| A hands-on introduction to natural language processing in Python | Grishma Jena (IBM) | O’Reilly Open Source Software Conference 2019 |
| From monolith to microservices: Design, build, deploy, learn | Elmer Thomas (Twilio SendGrid), Craig Dennis (Twilio) | O’Reilly Open Source Software Conference 2019 |
| Let’s go serverless with Swift using Vapor | Timirah James (TechniGal LA) | O’Reilly Open Source Software Conference 2019 |
| Model as a service for real-time decisioning | Niraj Tank (Capital One), Sumit Daryani (Capital One) | O’Reilly Open Source Software Conference 2019 |
| O’Reilly Open Source and Frank Willison Awards | O’Reilly Open Source Software Conference 2019 | |
| Polyglot applications with GraalVM | Michael Hunger (Neo4j) | O’Reilly Open Source Software Conference 2019 |
| Removing unfair bias in machine learning using open source (sponsored by IBM) | ANA ECHEVERRI (IBM), Trisha Mahoney (IBM) | O’Reilly Open Source Software Conference 2019 |
| The OS for AI: How serverless computing enables the next gen of machine learning | Jonathan Peck (GitHub) | O’Reilly Open Source Software Conference 2019 |
| Untangling the knots with distributed tracing | Isobel Redelmeier (LightStep) | O’Reilly Open Source Software Conference 2019 |
| Building a sales AI platform: Key principles and lessons learned | Moty Fania (Intel) | Strata Data Conference 2019 |
| Continuous intelligence: Moving machine learning into production reliably | Danilo Sato (ThoughtWorks), Christoph Windheuser (ThoughtWorks) | Strata Data Conference 2019 |
| LSTM-based time series anomaly detection using Analytics Zoo for Spark and BigDL | Guoqiong Song (Intel) | Strata Data Conference 2019 |
| Migrating Apache Oozie workflows to Apache Airflow | Feng Lu (Google Cloud), James Malone (Google), Apurva Desai (Google Cloud), Cameron Moberg (Truman State University | Google Cloud) |
| Better Together Diversity Networking Lunch | O’Reilly Artificial Intelligence Conference 2019 | |
| Deep learning methods for natural language processing | Garrett Hoffman (StockTwits) | O’Reilly Artificial Intelligence Conference 2019 |
| Applications of mixed effects random forests | Sourav Dey (Manifold) | Strata Data Conference 2019 |
| Faster ML over joins of tables | Arun Kumar (University of California, San Diego) | Strata Data Conference 2019 |
| New directions in record linkage | Yves Thibaudeau (US Census Bureau) | Strata Data Conference 2019 |
| Technical debt: A master class | r0ml Lefkowitz (Retired) | O’Reilly Software Architecture Conference 2019 |
| The Elements of Kubernetes: Foundational concepts for apps running on Kubernetes | Aaron Schlesinger (Microsoft) | O’Reilly Software Architecture Conference 2019 |
| Automating “Network Ready for Use” Testing | Jeremy Schulman | NANOG77 2019 |
| Powering Your Automation: A Single Source of Truth | Tim Schreyack, Dell Networking | NANOG77 2019 |
| Extending Salt’s capabilities for event-driven network automation and orchestration | Mircea Ulinic, DigitalOcean | NANOG76 2019 |
| Using open source tools to validate network configuration | Daniel Halperin, Intentionet, Inc. | NANOG75 2019 |
| Lessons Learned from the Migration to Apache Airflow | Radek Maciaszek (Chief Architect, Skimlinks) | Open Source Summit + ELC North America 2019 |
| IPMI is Dead, Long Live Redfish | Bruno Cornec (Open Source & Technology Strategist, HPE) | Open Source Summit + ELC North America 2019 |
| Learning the Linux Kernel Configuration Space: Results and Challenges | Mathieu Acher (Professor, University of Rennes) | Open Source Summit + ELC Europe 2019 |
| Developing Operators with the Kubernetes Operator Pythonic Framework (kopf) | Sergey Vasilyev (Senior Backend Engineer, Zalando SE) | KubeCon + CloudNativeCon North America 2019 |
| Use Your Favorite Developer Tools in Kubernetes With Telepresence | Abhay Saxena (Principal Software Engineer, Datawire) | KubeCon + CloudNativeCon North America 2019 |
| Lightning Talk: Using Jupyter Notebooks To Gain Insight Of Your Cluster | Ruben D Orduz (Member Technical Staff, VMware) | KubeCon + CloudNativeCon Europe 2019 |
| Tutorial: Introduction to Kubeflow Pipelines | Michelle Casbon (Senior Engineer, Google), Dan Anghel (Strategic Cloud Engineer, Google), Michal Zylinski (Cloud Customer Engineer, Google), Dan Sanche (Developer Programs Engineer, Google) | KubeCon + CloudNativeCon Europe 2019 |
| GPU Machine Learning From Laptop to Cloud | Mark Puddick (Advisory Platform Architect, Pivotal) | KubeCon + CloudNativeCon Europe 2019 |
Talks in 2018
| A day in the life of a data scientist in an AI company | Francesca Lazzeri (Microsoft), Jaya Susan Mathew (Microsoft) | Artificial Intelligence Conference 2018 |
| Reinforcement Learning Coach | Gal Novik (Intel AI) | Artificial Intelligence Conference 2018 |
| Sell cron, buy Airflow: Modern data pipelines in finance | James Meickle (Quantopian) | O’Reilly Velocity Conference 2018 |
| A day in the life of a data scientist: How do we train our teams to get started with AI? | Francesca Lazzeri (Microsoft), Jaya Susan Mathew (Microsoft) | Strata Data Conference 2018 |
| Bighead: Airbnb’s end-to-end machine learning platform | Atul Kale (Airbnb), Xiaohan Zeng (Airbnb) | Strata Data Conference 2018 |
| Conda, Docker, and Kubernetes: The cloud-native future of data science (sponsored by Anaconda) | Mathew Lodge (Anaconda) | Strata Data Conference 2018 |
| Conda, Docker, and Kubernetes: The cloud-native future of data science (sponsored by Anaconda) | Mathew Lodge (Anaconda) | Strata Data Conference 2018 |
| Frontiers of TensorFlow: Space, statistics, and probabilistic ML (sponsored by Google) | Joshua Dillon (Google Research), Wahid Bhimji (NERSC) | Artificial Intelligence Conference 2018 |
| Neural Network Distiller: A PyTorch environment for neural network compression | Neta Zmora (Intel AI Lab) | Artificial Intelligence Conference 2018 |
| Design and analysis of the world’s most advanced microprocessors using Jupyter notebooks | Kerim Kalafala (IBM), NICHOLAI L’ESPERANCE (IBM) | JupyterCon in New York 2018 2018 |
| Designing for interaction | Scott Sanderson (Quantopian) | JupyterCon in New York 2018 2018 |
| I don’t like notebooks. | Joel Grus (Allen Institute for Artificial Intelligence) | JupyterCon in New York 2018 2018 |
| Jupyter for every high schooler | Rob Newton (Trinity School) | JupyterCon in New York 2018 2018 |
| Jupyter graduates | Douglas Blank (Comet.ML) | JupyterCon in New York 2018 2018 |
| Jupyter’s configuration system | Afshin Darian (Two Sigma | Project Jupyter), M Pacer (Netflix), Min Ragan-Kelley (Simula Research Laboratory), Matthias Bussonnier (UC Berkeley BIDS) |
| JupyterHub for domain-focused integrated learning modules | Mariah Rogers (UC Berkeley Division of Data Sciences), Julian Kudszus (UC Berkeley Division of Data Sciences) | JupyterCon in New York 2018 2018 |
| nbinteract: Shareable interactive web pages from notebooks | Sam Lau (UC Berkeley), Caleb Siu (UC Berkeley) | JupyterCon in New York 2018 2018 |
| Pangeo: Big data climate science in the cloud | Ryan Abernathey (Columbia University), Yuvi Panda (Data Science Education Program (UC Berkeley)) | JupyterCon in New York 2018 2018 |
| Reproducible data dependencies for Jupyter: Distributing massive, versioned image datasets from the Allen Institute for Cell Science | Jackson Brown (Allen Institute for Cell Science), Aneesh Karve (Quilt) | JupyterCon in New York 2018 2018 |
| Reproducible quantum chemistry in Jupyter | Chris Harris (Kitware) | JupyterCon in New York 2018 2018 |
| SoS: A polyglot notebook and workflow system for both interactive multilanguage data analysis and batch data processing | Bo Peng (The University of Texas, MD Anderson Cancer Center) | JupyterCon in New York 2018 2018 |
| The Emacs Ipython Notebook | John Miller (Honeywell UOP) | JupyterCon in New York 2018 2018 |
| The reporter’s notebook | mark hansen (Columbia Journalism School | The Brown Institute for Media Innovation) |
| Developing chatbots for Mycroft and his virtual friends | Laurie Hannon (SoftSource Consulting) | O’Reilly Open Source Convention 2018 |
| Deep learning 101: Apache MXNet | Simon Corston-Oliver (AWS) | O’Reilly Open Source Convention 2018 |
| Managing SDKs and their communities in multiple programming languages | Elmer Thomas (Twilio SendGrid) | O’Reilly Open Source Convention 2018 |
| One-off wearables: The Linux steampunk conference badge | Rob Reilly (Rob “drtorq” Reilly) | O’Reilly Open Source Convention 2018 |
| Open sourcing quantum: Get ready to help build a new future | Jay Gambetta (IBM) | O’Reilly Open Source Convention 2018 |
| The async invasion | Stephen Cleary (Faithlife) | O’Reilly Open Source Convention 2018 |
| Detecting small-scale mines in Ghana | Elena Terenzi (Microsoft), Michael Lanzetta (Microsoft) | Strata Data Conference 2018 |
| Human-in-the-loop data science with Jupyter widgets | Pascal Bugnion (ASI Data Science) | Strata Data Conference 2018 |
| Machine-learned model quality monitoring in fast data and streaming applications | Emre Velipasaoglu (Lightbend) | Strata Data Conference 2018 |
| Scaling the AI hierarchy of needs with TensorFlow, Spark, and Hops | Jim Dowling (Logical Clocks) | Strata Data Conference 2018 |
| 20 Netflix-style principles and practices to get the most out of your data platform | Kurt Brown (Netflix) | Strata Data Conference 2018 |
| 20 Netflix-style principles and practices to get the most out of your data platform | Kurt Brown (Netflix) | Strata Data Conference 2018 |
| Achieving GDPR compliance and data privacy using blockchain technology | Ajay Kumar Mothukuri (Sapient), Vijay Agneeswaran (Walmart Labs) | Strata Data Conference 2018 |
| Data science in the cloud | Alex Smola (Amazon) | Strata Data Conference 2018 |
| Machine-learned model quality monitoring in fast data and streaming applications | Emre Velipasaoglu (Lightbend) | Strata Data Conference 2018 |
| Playing well together: Big data beyond the JVM with Spark and friends | Holden Karau (Independent), Rachel Warren (Salesforce Einstein) | Strata Data Conference 2018 |
| Technical debt: A master class | Robert Lefkowitz (Warby Parker) | O’Reilly Software Architecture Conference 2018 |
| Automating Device Certifications with Robot Framework | Pratik Lotia, Charter Communications | NANOG74 2018 |
| Scaling the Facebook backbone through Zero Touch Provisioning (ZTP) | Brandon Bennett, Facebook, David Swafford, Facebook | NANOG73 2018 |
| Package Management and Distribution in a Cloud World | Jose Miguel Parrella (Principal Program Manager, Microsoft Azure, Microsoft) | Automotive Linux Summit & Open Source Summit Japan 2018 |
| Power Debugging with JTAG | Alexandre Bailon (Embedded Linux Kernel Senior Developper, BAYLIBRE), Patrick Titiano (SW Director, BayLibre) | Open Source Summit + ELC Europe 2018 |
| Airflow on Kubernetes: Dynamic Workflows Simplified | Daniel Imberman (Senior Software Engineer, Bloomberg), Barni Seetharaman (Senior SWE, Google) | KubeCon + CloudNativeCon North America 2018 |
Talks in 2017
| Web security analysis toolbox | Ido Safruti (PerimeterX), Amir Shaked (PerimeterX) | O’Reilly Security Conference 2017 |
| You had one job! Learning to cope with failures in a complex distributed system | Ed Hiley (NHS Digital), Dan Rathbone (Infinity Works) | O’Reilly Velocity Conference 2017 |
| A hands-on data science crash course for modeling and predicting the behavior of (large) distributed systems | Bart De Vylder (CoScale), Pieter Buteneers (CoScale) | O’Reilly Velocity Conference 2017 |
| Learning from higher education: How Ivy Tech is using predictive analytics and data democracy to reverse decades of entrenched practices | Brendan Aldrich (Ivy Tech Community College ), Lige Hensley (Ivy Tech Community College ) | Strata Data Conference 2017 |
| PyTextRank: Graph algorithms for enhanced natural language processing | Paco Nathan (derwen.ai) | Strata Data Conference 2017 |
| The columnar roadmap: Apache Parquet and Apache Arrow | Julien Le Dem (WeWork) | Strata Data Conference 2017 |
| Accelerating data-driven culture at the largest media group in Latin America with Jupyter | Diogo Munaro Vieira (Globo.com), Felipe Ferreira (Globo.com) | JupyterCon in New York 2017 2017 |
| Beautiful networks and network analytics made simpler with Jupyter | Daina Bouquin (Harvard-Smithsonian Center for Astrophysics), John D (CUNY Building Performance Lab) | JupyterCon in New York 2017 2017 |
| Building a powerful data science IDE for R, Python, and SQL using JupyterLab | Ali Marami (R-Brain Inc) | JupyterCon in New York 2017 2017 |
| Deploying a reproducible course | Lindsey Heagy (University of British Columbia), Rowan Cockett (3point Science) | JupyterCon in New York 2017 2017 |
| Design for reproducibility | Lorena Barba (George Washington University) | JupyterCon in New York 2017 2017 |
| GeoNotebook: An extension to the Jupyter Notebook for exploratory geospatial analysis | Christopher Kotfila (Kitware) | JupyterCon in New York 2017 2017 |
| Jupyter notebooks and production data science workflows | Andrew Therriault (City of Boston) | JupyterCon in New York 2017 2017 |
| Mapping data in Jupyter notebooks with PixieDust (sponsored by IBM) | RAJ SINGH (IBM Cloud Data Services) | JupyterCon in New York 2017 2017 |
| Notebook narratives from industry: Inspirational real-world examples and reusable industry notebooks | Patty Ryan (Microsoft), Lee Stott (Microsoft), Michael Lanzetta (Microsoft) | JupyterCon in New York 2017 2017 |
| Project Jupyter: From interactive Python to open science | Fernando Perez (UC Berkeley and Lawrence Berkeley National Laboratory) | JupyterCon in New York 2017 2017 |
| Scala: Why hasn’t an official Scala kernel for Jupyter emerged yet? | Alexandre Archambault (Teads.tv) | JupyterCon in New York 2017 2017 |
| A hands-on data science crash course for modeling and predicting the behavior of (large) distributed systems | Bart De Vylder (CoScale) | O’Reilly Velocity Conference 2017 |
| Predictive system performance data analysis (sponsored by Salesforce) | Jasmin Nakic (Salesforce ), Samir Pilipovic (Salesforce) | O’Reilly Velocity Conference 2017 |
| TensorFlow and deep learning (without a PhD) | Martin Görner (Google) | Strata Data Conference 2017 |
| Automated data exploration: Building efficient analysis pipelines with dask | Victor Zabalza (ASI Data Science) | Strata Data Conference 2017 |
| Spark and R with sparklyr | Douglas Ashton (Mango Solutions), Aimee Gott (Mango Solutions), Mark Sellors (Mango Solutions) | Strata Data Conference 2017 |
| Building a real-time recommendation engine with Neo4j | William Lyon (Neo4j) | O’Reilly Open Source Convention 2017 |
| Databases and Docker: A survival guide | Alvin Richards (MariaDB Corporation) | O’Reilly Open Source Convention 2017 |
| Developer on the rise: Blurring the line between developer and data scientist with PixieDust | va barbosa (IBM) | O’Reilly Open Source Convention 2017 |
| Distinguish pop music from heavy metal using Apache Spark MLlib | Taras Matyashovsky (Lohika) | O’Reilly Open Source Convention 2017 |
| O’Reilly Open Source and Frank Willison Awards | O’Reilly Open Source Convention 2017 | |
| Rust for non-Rust developers | Hanneli Tavante (Codemine42) | O’Reilly Open Source Convention 2017 |
| Fear of and uncertainty about open source | Wes Chow (Cortico at MIT Media Lab) | O’Reilly Software Architecture Conference 2017 |
| Big data for big data: Machine-learning models of Hadoop cluster behavior | Sean Suchter (Pepperdata), Shekhar Gupta (Pepperdata) | Strata + Hadoop World 2017 |
| PyTorch: A flexible and intuitive framework for deep learning | James Bradbury (Salesforce Research) | Strata + Hadoop World 2017 |
| Saving lives with data: Identifying patients at risk of decline | . . (ProKarma) | Strata + Hadoop World 2017 |
| Shifting left for continuous quality in an Agile data world | Avinash Padmanabhan (Intuit) | Strata + Hadoop World 2017 |
| Network Automation: past, present, and future | Mircea Ulinic, Cloudflare, David Barroso, Fastly, Jathan McCollum, Dropbox, Jason Edelman, Network to Code, Jeremy Stretch, Digital Ocean, Kirk Byers, Twin Bridges Technology | NANOG71 2017 |
| Command Execution in Heterogeneous Network at Facebook scale | Surinder Singh, Facebook Inc. | NANOG71 2017 |
| Network Telemetry at Yahoo! | Matt Hudgins (Yahoo!) , Varun Varma (Yahoo) | NANOG70 2017 |
| Network Automation at scale: up and running in 60 minutes | Mircea Ulinic (CloudFlare) | NANOG69 2017 |
| Why Go? | James Boswell | NANOG69 2017 |
| Diversity in Open Source: No Longer at Square One | Marina Zhurakhinskaya (Senior Outreach Specialist, Red Hat) | Open Source Summit North America 2017 |
| Keynote: Hacking is Child’s Play, Literally! | Reuben Paul (11 Year Old Hacker, CyberShaolin Founder and Cyber Security Ambassador) | Open Source Summit Europe 2017 |
| Everything You Always Wanted to Know About Object Storage | Orit Wasserman (Senior Principal Software Engineer, Red Hat) | Open Source Summit Europe 2017 |
| Pipeline as Code For Your Infrastructure as Code | Kris Buytaert (Chief Yak Shaver, Inuits.eu) | Open Source Summit Europe 2017 |
| Everything You Always Wanted to Know About Object Storage | Orit Wasserman (Senior Principal Software Engineer, Red Hat) | Open Source Summit Europe 2017 |
| Integration of Flexible Storage with the API of Gluster | Niels de Vos (Senior Software Engineer, Red Hat) | Open Source Summit Europe 2017 |
| Bringing People Together with Open Source | Ori Rabin (Sr. Software Engineer, Red Hat), Freddy Rolland (Senior Software Engineer, Red Hat) | Open Source Summit Europe 2017 |
| The Elements of Kubernetes - Foundational Concepts for Apps Running on Kubernetes [I] | Aaron Schlesinger (Cloud Developer Advocate, Microsoft) | KubeCon + CloudNativeCon North America 2017 |
| Modern Big Data Pipelines over Kubernetes [I] | Eliran Bivas (Senior Big Data Architect, iguazio) | KubeCon + CloudNativeCon North America 2017 |
Talks in 2016
| Cloud architectures for data science | O’Reilly Software Architecture Conference 2016 | |
| Chainer: A flexible and intuitive framework for complex neural networks | Orion Wolfe (Preferred Networks), Shohei Hido (Preferred Networks) | O’Reilly Artificial Intelligence Conference 2016 |
| Sell cron, buy Airflow: Modern data pipelines in finance | James Meickle (Quantopian) | Velocity 2016 |
| Robust anomaly detection for real user monitoring data | Ritesh Maheshwari (LinkedIn), Yang Yang (LinkedIn) | Velocity 2016 |
| Fluent Python: Implementing intuitive and productive APIs | Luciano Ramalho (ThoughtWorks) | O’Reilly Open Source Convention 2016 |
| Inessential weirdnesses in open source | Sumana Harihareswara (Changeset Consulting) | O’Reilly Open Source Convention 2016 |
| Navigating the data science Python ecosystem | Christine Doig (Continuum Analytics) | O’Reilly Open Source Convention 2016 |
| Docker for data scientists | Michelangelo D’Agostino (ShopRunner) | Strata + Hadoop World 2016 |
| Filling the data lake | Chuck Yarbrough (Pentaho), Mark Burnette (Pentaho, a Hitachi Group Company) | Strata + Hadoop World 2016 |
| Python scalability: A convenient truth | Travis Oliphant (Continuum Analytics) | Strata + Hadoop World 2016 |
| Scalable schema management for Hadoop and Spark applications | Kelvin Chu (Uber), Evan Richards (Uber) | Strata + Hadoop World 2016 |
| Ok, We Got YANG Data Models, Now What? | Santiago Alvarez | NANOG68 2016 |
| NetOps Coding 101 - Python Intro and Regular Expression Deep Dive | David Swafford (Facebook) | NANOG66 2016 |
| NetOps Coding 101 - Python Intro and Regular Expression Deep Dive (Part 2 of 2) | David Swafford (Facebook) | NANOG66 2016 |
| NetOps Coding 201 - Building Facebook’s FBAR for Network Devices | David Swafford (Facebook) | NANOG66 2016 |
