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Title & Speakers	Event
PyData Southampton - 10th Meetup 2024-10-15 · 18:00 Venue: Carnival House, 100 Harbour Parade, Southampton, SO15 1ST 📢 Want to speak 📢: submit your talk proposal Main Talks 1️⃣ Visual Place Recognition - Emily Miller Visual Place Recognition (VPR) is a critical task in robotics and autonomous systems, enabling machines to recognise and localise themselves within an environment using visual cues. This talk will dive into the key concepts of VPR, with a focus on Python-based tools and libraries that simplify the implementation of VPR algorithms. We'll explore feature extraction techniques, the role of deep learning in advancing VPR, and practical applications for real-world problems. Whether you're building autonomous drones or smart city solutions, this talk will provide insights on using Python to develop robust and scalable VPR systems. 2️⃣ Faster Models, Faster Answers: Discover Emulation for Your Workflow - Austen Wallis AI this and AI that, the world in the past couple of years has become overrun with news of Generative AI and the ever-improving odds of a takeover from our new robot overlord, ChatGPT. However, have you ever heard about Generative Modelling? This research field is no longer just about creating pretty pictures and making funky tunes about your favourite branded baked beans. No, step with me into the world of Emulation! We’ll probe how simple generative deep-learning models can improve complex physics simulations to not only be rapid but quick as a flash. So, fasten your seat belts as I take you on an ultra-fast whistle-stop tour exploring the universe of surrogate modelling, neural networks and the latent space. As I showcase the raw power of emulators, we’ll uncover how faster models unlock new answers (and questions) in both Astrophysics and fusion energy. Also, we’ll examine introductory examples of how you can build your own emulator from scratch. For an evening event, you don’t want to miss, I look forward to seeing you there ... and bring a fire extinguisher; my GPU will be on fire 🔥! Lightning Talks ⚡ ⚡1️⃣ TBD ⚡2️⃣ TBD Please note: 🚨🚨🚨A valid photo ID is required by building security. You MUST use your initial/first name and surname on your meetup profile, otherwise, you will NOT make it on the guest list! 🚨🚨🚨 This event follows the NumFOCUS Code of Conduct, please familiarise yourself with it before the event. If your RSVP status says "You're going" you will be able to get in. No further confirmation required. You will NOT need to show your RSVP confirmation when signing in. If you can no longer make it, please unRSVP as soon as you know so we can assign your place to someone on the waiting list. * Code of Conduct: This event follows the NumFOCUS Code of Conduct, please familiarise yourself with it before the event. Please get in touch with the organisers with any questions or concerns regarding the Code of Conduct. * There will be pizza & drinks, generously provided by our host, Carnival UK. * Logistics** Doors open at 6.30 pm, talks start at 7 pm. For those who wish to continue networking and chatting we will move to a nearby pub/bar for drinks from 9 pm. Please unRSVP in good time if you realise you can't make it. We're limited by building security on the number of attendees, so please free up your place for your fellow community members! Follow @pydatasoton (https://twitter.com/pydatasoton) for updates and early announcements. We are also on Instagram/Threads as @pydatasoton, and find us on LinkedIn.	PyData Southampton - 10th Meetup
Of Checklists, Ethics, and Data with Emily Miller and Peter Bull (Cross Post from Podcast.__init__) - Episode 53 2018-10-22 · 02:00 Emily Miller – guest @ Driven Data , Peter Bull – guest @ Driven Data , Tobias Macey – host Summary As data science becomes more widespread and has a bigger impact on the lives of people, it is important that those projects and products are built with a conscious consideration of ethics. Keeping ethical principles in mind throughout the lifecycle of a data project helps to reduce the overall effort of preventing negative outcomes from the use of the final product. Emily Miller and Peter Bull of Driven Data have created Deon to improve the communication and conversation around ethics among and between data teams. It is a Python project that generates a checklist of common concerns for data oriented projects at the various stages of the lifecycle where they should be considered. In this episode they discuss their motivation for creating the project, the challenges and benefits of maintaining such a checklist, and how you can start using it today. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat This is your host Tobias Macey and this week I am sharing an episode from my other show, Podcast.init, about a project from Driven Data called Deon. It is a simple tool that generates a checklist of ethical considerations for the various stages of the lifecycle for data oriented projects. This is an important topic for all of the teams involved in the management and creation of projects that leverage data. So give it a listen and if you like what you hear, be sure to check out the other episodes at pythonpodcast.com Interview Introductions How did you get introduced to Python? Can you start by describing what Deon is and your motivation for creating it? Why a checklist, specifically? What’s the advantage of this over an oath, for example? What is unique to data science in terms of the ethical concerns, as compared to traditional software engineering? What is the typical workflow for a team that is using Deon in their projects? Deon ships with a default checklist but allows for customization. What are some common addendums that you have seen? Have you received pushback on any of the default items? How does Deon simplify communication around ethics across team boundaries? What are some of the most often overlooked items? What are some of the most difficult ethical concerns to comply with for a typical data science project? How has Deon helped you at Driven Data? What are the customer facing impacts of embedding a discussion of ethics in the product development process? Some of the items on the default checklist coincide with regulatory requirements. Are there any cases where regulation is in conflict with an ethical concern that you would like to see practiced? What are your hopes for the future of the Deon project? Keep In Touch Emily LinkedIn ejm714 on GitHub Peter LinkedIn @pjbull on Twitter pjbull on GitHub Driven Data @drivendataorg on Twitter drivendataorg on GitHub Website Picks Tobias Richard Bond Glass Art Emily Tandem Coffee in Portland, Maine Peter The Model Bakery in Saint Helena and Napa, California Links Deon Driven Data International Development Brookings Institution Stata Econometrics Metis Bootcamp Pandas Podcast Episode C# .NET Podcast.init Episode On Software Ethics Jupyter Notebook Podcast Episode Word2Vec cookiecutter data science Logistic Regression The intro and outro music is API Data Engineering Data Management Data Science GitHub Pandas Python	Data Engineering Podcast Listen

Title & Speakers

Event

PyData Southampton - 10th Meetup 2024-10-15 · 18:00

Venue: Carnival House, 100 Harbour Parade, Southampton, SO15 1ST 📢 Want to speak 📢: submit your talk proposal

Main Talks 1️⃣ Visual Place Recognition - Emily Miller Visual Place Recognition (VPR) is a critical task in robotics and autonomous systems, enabling machines to recognise and localise themselves within an environment using visual cues. This talk will dive into the key concepts of VPR, with a focus on Python-based tools and libraries that simplify the implementation of VPR algorithms. We'll explore feature extraction techniques, the role of deep learning in advancing VPR, and practical applications for real-world problems. Whether you're building autonomous drones or smart city solutions, this talk will provide insights on using Python to develop robust and scalable VPR systems.

2️⃣ Faster Models, Faster Answers: Discover Emulation for Your Workflow - Austen Wallis AI this and AI that, the world in the past couple of years has become overrun with news of Generative AI and the ever-improving odds of a takeover from our new robot overlord, ChatGPT. However, have you ever heard about Generative Modelling? This research field is no longer just about creating pretty pictures and making funky tunes about your favourite branded baked beans. No, step with me into the world of Emulation! We’ll probe how simple generative deep-learning models can improve complex physics simulations to not only be rapid but quick as a flash. So, fasten your seat belts as I take you on an ultra-fast whistle-stop tour exploring the universe of surrogate modelling, neural networks and the latent space. As I showcase the raw power of emulators, we’ll uncover how faster models unlock new answers (and questions) in both Astrophysics and fusion energy. Also, we’ll examine introductory examples of how you can build your own emulator from scratch.

For an evening event, you don’t want to miss, I look forward to seeing you there ... and bring a fire extinguisher; my GPU will be on fire 🔥!

Lightning Talks ⚡ ⚡1️⃣ TBD ⚡2️⃣ TBD

Please note:

🚨🚨🚨A valid photo ID is required by building security. You MUST use your initial/first name and surname on your meetup profile, otherwise, you will NOT make it on the guest list! 🚨🚨🚨
This event follows the NumFOCUS Code of Conduct, please familiarise yourself with it before the event.

If your RSVP status says "You're going" you will be able to get in. No further confirmation required. You will NOT need to show your RSVP confirmation when signing in. If you can no longer make it, please unRSVP as soon as you know so we can assign your place to someone on the waiting list.

*** Code of Conduct: This event follows the NumFOCUS Code of Conduct, please familiarise yourself with it before the event. Please get in touch with the organisers with any questions or concerns regarding the Code of Conduct. *** There will be pizza & drinks, generously provided by our host, Carnival UK. ***

Logistics Doors open at 6.30 pm, talks start at 7 pm. For those who wish to continue networking and chatting we will move to a nearby pub/bar for drinks from 9 pm.

Please unRSVP in good time if you realise you can't make it. We're limited by building security on the number of attendees, so please free up your place for your fellow community members!

Follow @pydatasoton (https://twitter.com/pydatasoton) for updates and early announcements. We are also on Instagram/Threads as @pydatasoton, and find us on LinkedIn.

PyData Southampton - 10th Meetup

Of Checklists, Ethics, and Data with Emily Miller and Peter Bull (Cross Post from Podcast.__init__) - Episode 53 2018-10-22 · 02:00

Emily Miller – guest @ Driven Data , Peter Bull – guest @ Driven Data , Tobias Macey – host

Summary

As data science becomes more widespread and has a bigger impact on the lives of people, it is important that those projects and products are built with a conscious consideration of ethics. Keeping ethical principles in mind throughout the lifecycle of a data project helps to reduce the overall effort of preventing negative outcomes from the use of the final product. Emily Miller and Peter Bull of Driven Data have created Deon to improve the communication and conversation around ethics among and between data teams. It is a Python project that generates a checklist of common concerns for data oriented projects at the various stages of the lifecycle where they should be considered. In this episode they discuss their motivation for creating the project, the challenges and benefits of maintaining such a checklist, and how you can start using it today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat This is your host Tobias Macey and this week I am sharing an episode from my other show, Podcast.init, about a project from Driven Data called Deon. It is a simple tool that generates a checklist of ethical considerations for the various stages of the lifecycle for data oriented projects. This is an important topic for all of the teams involved in the management and creation of projects that leverage data. So give it a listen and if you like what you hear, be sure to check out the other episodes at pythonpodcast.com

Interview

Introductions How did you get introduced to Python? Can you start by describing what Deon is and your motivation for creating it? Why a checklist, specifically? What’s the advantage of this over an oath, for example? What is unique to data science in terms of the ethical concerns, as compared to traditional software engineering? What is the typical workflow for a team that is using Deon in their projects? Deon ships with a default checklist but allows for customization. What are some common addendums that you have seen?

Have you received pushback on any of the default items?

How does Deon simplify communication around ethics across team boundaries? What are some of the most often overlooked items? What are some of the most difficult ethical concerns to comply with for a typical data science project? How has Deon helped you at Driven Data? What are the customer facing impacts of embedding a discussion of ethics in the product development process? Some of the items on the default checklist coincide with regulatory requirements. Are there any cases where regulation is in conflict with an ethical concern that you would like to see practiced? What are your hopes for the future of the Deon project?

Keep In Touch

Emily

LinkedIn ejm714 on GitHub

Peter

LinkedIn @pjbull on Twitter pjbull on GitHub

Driven Data

@drivendataorg on Twitter drivendataorg on GitHub Website

Picks

Tobias

Richard Bond Glass Art

Emily

Tandem Coffee in Portland, Maine

Peter

The Model Bakery in Saint Helena and Napa, California

Links

Deon Driven Data International Development Brookings Institution Stata Econometrics Metis Bootcamp Pandas

Podcast Episode

C# .NET Podcast.init Episode On Software Ethics Jupyter Notebook

Podcast Episode

Word2Vec cookiecutter data science Logistic Regression

The intro and outro music is

API Data Engineering Data Management Data Science GitHub Pandas Python

Data Engineering Podcast

Listen

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