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Title & Speakers	Event
Event AI and Deep Learning for Enterprise #14 2024-03-13
Real-world AI: a survey of practitioners 2024-03-13 · 18:30 michael natusch – ex Chief Science Officer @ Prudential Real-world AI: a survey of practitioners. AI/ML
Building models on real-time data with Jupyter and Streambased 2024-03-13 · 18:30 Tom Scott – Founder & CEO @ Streambased Discussion on building models on real-time data using Jupyter notebooks and Streambased. jupyter streambased
Introduction to Geospatial Information Systems 2024-03-13 · 18:30 ramani lachyan – Data Scientist @ Datasparq Overview of geospatial information systems concepts and applications.

PyData London - 77th meetup 2023-09-06 · 18:00 Venue: 1 Angel Lane, EC4R 3AB Please note: 🚨🚨🚨A valid photo ID is required by building security. You MUST use your full real names 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. Tickets are assigned through a lottery draw about 1 week 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. As always, there'll be free food & drinks, generously provided by our host, Man Group. Main Talks 1️⃣ Turning your Data/AI algorithms into full web apps in no time with Python - Marine Gosselin Who hasn't heard of the "Pilot Syndrom"? 85% of Data Science Pilots remain pilots and do not make it to the production stage. Let's build a production-ready and end-user-friendly Data Science application. 100% python and 100% open source. Phase 1 \\| Building the GUI: create an interactive and powerful interface in a few lines of code Phase 2 \\| Integrated back end: Manage your models\, and pipelines and create scenarios the smart way 2️⃣ 10 rules on how to mess up your ML implementation - Michael Natusch Leo Tolstoy famously wrote in Anna Karenina that “all happy families are alike; each unhappy family is unhappy in its own way.” Something very similar holds for machine learning: there are many ways to get your ML implementation wrong, but just a few in which they succeed, achieving their business goals and making our stakeholders happy. While following the latest LLM is tempting, there are more important considerations. This talk dissects the ten rules of how to get an ML implementation wrong, learnt painfully in the course of ten years by the speaker. These rules cover areas such as the prioritisation of AI use cases, how data is at the heart of everything we do, the choice of models, the role of design and the ongoing management of the end-to-end AI system. Keeping these rules in mind helps us practitioners implement ML successfully. Lightning Talks ⚡ ⚡1️⃣ Finetuning LLaMA on custom dataset in 3 steps - Aniket Maurya ⚡2️⃣ Using Python in Evidence Based Policing - Successes and Challenges - Andreas Varotsis Logistics Doors open at 6.30 pm (get there early as you have to sign-in via building security), talks start at 7 pm, drinks from 9 pm in the bar. We will have reduced capacity for this event but there will be plenty of people to discuss data science questions with! 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 @pydatalondon (https://twitter.com/pydatalondon) for updates and early announcements.	PyData London - 77th meetup

Venue: 1 Angel Lane, EC4R 3AB

Please note:

🚨🚨🚨A valid photo ID is required by building security. You MUST use your full real names 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.

Tickets are assigned through a lottery draw about 1 week 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.

As always, there'll be free food & drinks, generously provided by our host, Man Group.

Main Talks

1️⃣ Turning your Data/AI algorithms into full web apps in no time with Python - Marine Gosselin

Who hasn't heard of the "Pilot Syndrom"? 85% of Data Science Pilots remain pilots and do not make it to the production stage. Let's build a

production-ready and end-user-friendly Data Science application. 100% python and 100% open source.

Phase 1 \| Building the GUI: create an interactive and powerful interface in a few lines of code

Phase 2 \| Integrated back end: Manage your models\, and pipelines and create scenarios the smart way

2️⃣ 10 rules on how to mess up your ML implementation - Michael Natusch

Leo Tolstoy famously wrote in Anna Karenina that “all happy families are alike; each unhappy family is unhappy in its own way.” Something very similar holds for machine learning: there are many ways to get your ML implementation wrong, but just a few in which they succeed, achieving their business goals and making our stakeholders happy. While following the latest LLM is tempting, there are more important considerations. This talk dissects the ten rules of how to get an ML implementation wrong, learnt painfully in the course of ten years by the speaker. These rules cover areas such as the prioritisation of AI use cases, how data is at the heart of everything we do, the choice of models, the role of design and the ongoing management of the end-to-end AI system. Keeping these rules in mind helps us practitioners implement ML successfully.

Lightning Talks ⚡

⚡1️⃣ Finetuning LLaMA on custom dataset in 3 steps - Aniket Maurya

⚡2️⃣ Using Python in Evidence Based Policing - Successes and Challenges - Andreas Varotsis

Logistics

Doors open at 6.30 pm (get there early as you have to sign-in via building security), talks start at 7 pm, drinks from 9 pm in the bar. We will have reduced capacity for this event but there will be plenty of people to discuss data science questions with!

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 @pydatalondon (https://twitter.com/pydatalondon) for updates and early announcements.

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