talk-data.com

When: Thursday 10th April 2025 Time: arrive for 5:45pm with talks starting from 6pm start prompt.

Location: BJSS, 90 Victoria St, Redcliffe, BS1 6DP

Complimentary drinks & pizza provided by our hosts BJSS

Social drinks afterwards will be at Ye Shakespeare (50m away) for those interested.

This #MSFABRICBRISTOL will feature Sue Bayes and Antony Catella

Session 1 - Unleashing Python in Fabric: Supercharging Data Workflows & Power BI - Sue Bayes Join us in Bristol for an immersive hour-long session that showcases the incredible power of Python within the Fabric environment. Discover how to effortlessly extract, clean, transform, and move data, and see how Python's versatility can streamline data workflows. In this demo-driven session, we’ll demonstrate how you can seamlessly integrate Python scripts with Fabric’s capabilities to create lightning-fast, scalable Power BI reports. We'll also explore the new semantic-link-labs functionality to show how easy it is to automate Power BI documentation and processes using the power of Python. From data manipulation to real-time insights, this session is perfect for data enthusiasts and Power BI professionals looking to take their reporting to the next level using Python. Don’t miss out on this hands-on opportunity to revolutionize your data processes!

About Sue Sue Bayes is a Microsoft Data Platform MVP with over six years of independent consulting experience as a data analyst and Power BI developer, helping businesses harness the full potential of their data. Certified as a Fabric Analytics Engineer, an Azure Enterprise Data Analyst and Power BI Data Analyst Associate, Sue is passionate about leveraging the latest tools in Microsoft Fabric to provide end-to-end data engineering solutions, with a strong emphasis on integrating Python into workflows. Her expertise spans a wide range of reporting solutions, from project management and financial reporting to bespoke data cleansing, sentiment analysis, and advanced data pipelines within the Fabric environment. Sue's technical skills include Python, SQL, R, and C#, but her real passion lies in Power BI's M language and DAX, which she uses to create high-performance, dynamic reports. Before becoming an independent consultant, Sue spent 15 years lecturing in Business and Computing, helping to shape the next generation of data professionals. Her deep understanding of data engineering, paired with a love for creating streamlined, actionable insights, has made her a sought-after expert in both the public and private sectors.

Session 2 - User Defined Formatting in Power BI - Antony Catella In general the formatting (column colours, display units, data label formats) in a Power BI report are fixed for the end user. How can we give end users the ability to format aspects of the visuals to suit their needs? In this session, we look at making use of dynamic format strings and conditional formatting in Power BI, to allow end users to change the formatting of visuals. We begin with the basic concepts of dynamic format strings, which allow for the customisation of displayed data based on context and user interactions. Attendees will then learn how to apply these strings in reports to enhance the readability of data visuals. Moving onto the use of conditional formatting rules and expressions to change the colours of data points within visuals. We also demonstrate combining dynamic format strings with conditional formatting to give end users significant flexibility in what to display in visuals. By the end of this session, participants will have a comprehensive understanding of how to leverage these features and include them for their report end users. About Antony Antony works as a BI Consultant for a consultancy based in the UK, developing Power BI reports for multiple clients, primarily Non-Profit Organisations. Power BI is a rapidly evolving technology which is what he loves about it - constantly learning new things.

We all look forward to seeing you there!!

By introducing a range of AI-enhanced products that amplify creativity and interactivity across our platforms, Buzzfeed has been able to connect with the largest global audience of young people online to cement its role as the defining digital media company of the AI era. Notably, some of Buzzfeed's most successful tools and content experiences thrive on the power of small, focused datasets. Still wondering how Shrek fits into the picture? You'll have to watch!

Video from: https://smalldatasf.com/

📓 Resources Big Data is Dead: https://motherduck.com/blog/big-data-... Small Data Manifesto: https://motherduck.com/blog/small-dat... Why Small Data?: https://benn.substack.com/p/is-excel-... Small Data SF: https://www.smalldatasf.com/

➡️ Follow Us LinkedIn: / motherduck
X/Twitter : / motherduck
Bluesky: motherduck.com Blog: https://motherduck.com/blog/

Discover how BuzzFeed's Data team, led by Gilad Cohen, harnesses AI for creative purposes, leveraging large language models (LLMs) and generative image capabilities to enhance content creation. This video explores how machine learning teams build tools to create new interactive media experiences, focusing on augmenting creative workflows rather than replacing jobs, allowing readers to participate more deeply in the content they consume.

We dive into the core data science problem of understanding what a piece of content is about, a crucial step for improving content recommendation systems. Learn why traditional methods fall short and how the team is constantly seeking smaller, faster, and more performant models. This exploration covers the evolution from earlier architectures like DistilBERT to modern, more efficient approaches for better content representation, clustering, and user personalization.

A key technique explored is the use of text embeddings, which are dense, low-dimensional vector representations of data. This video provides an accessible explanation of embeddings as a form of compressed knowledge, showing how BuzzFeed creates a unique vector for each article. This allows for simple vector math to find semantically similar content, forming a foundational infrastructure for powerful ranking and recommender systems.

Explore how BuzzFeed leverages generative image capabilities to create new interactive formats. The journey began with Midjourney experiments and evolved to building custom tools by fine-tuning a Stable Diffusion XL model using LORA (Low-Rank Approximation). This advanced technique provides greater control over image output, enabling the rapid creation of viral AI generators that respond to trending topics and allow for massive user engagement.

Finally, see a practical application of machine learning for content optimization. BuzzFeed uses its vast historical dataset from Bayesian A/B testing to train a model that predicts headline performance. By generating multiple headline candidates with an LLM like Claude and running them through this predictive model, they can identify the winning headline. This showcases how to use unique, in-house data to build powerful tools that improve click-through rates and drive engagement, pointing to a significant transformation in how media is created and consumed.

THIS IS AN ONLINE EVENT

[Connection details will be shared 1h before the start time]

The London Clojurians are happy to present: Title: Encoding internet-scale decentralised data structures on Convex Speaker: Mike Anderson Time: 2024-11-12 @ 18:30 (London time) Local time: click here for local time

Mike Anderson (https://www.linkedin.com/in/mike-cvx/) will be presenting: "Encoding internet-scale decentralised data structures on Convex"

This discussion will expand on the first Convex event with the London Clojurians (https://www.youtube.com/watch?v=bpKAQgcJRao&authuser=4) which provided an in-depth look at the whole platform. Now that Convex is ready to launch, the second conversation with dive into the significance of these efficient data formats and why the CAD003: Encoding Format (https://github.com/Convex-Dev/design/tree/develop/docs/cad/003_encoding) will be so revolutionary. The call will also cover examples for users. Note: the VM is written in Convex Lisp, inspired largely by Clojure. This past week Convex was awarded a grant from the EU government for a cross-chain token interoperability project.

Mike is a passionate contributor to open source software projects and an OG Clojurian, all the way back to Clojure 1.1 release. A programming enthusiast since the age of eight, Mike has a particular love for efficient data structures and algorithms. He represented the UK in the 1996 International Informatics Olympiad. Mike has held multiple CTO roles including being a founding member of Ocean Protocol (Blockchain for Data & AI) and Datacraft Sciences (Machine Learning, Data supply chain orchestration) based in Singapore.

If you missed this event, you can watch the recording on our YouTube channel: https://www.youtube.com/@LondonClojurians (The recording will be uploaded a couple of days after the event.)

Please, consider supporting the London Clojurians with a small donation: https://opencollective.com/london-clojurians/

Your contributions will enable the sustainability of the London Clojurians community and support our varied set of online and in-person events:

• ClojureBridge London: supports under-represented groups discover Clojure
• re:Clojure: our free to attend annual community conference
• monthly meetup events with speakers from all over the world
• subscription and admin costs such as domain name & StreamYard subscription

Thank you to our sponsors:

• https://juxt.pro/
• https://flexiana.com/
• And many individual sponsors

By introducing a range of AI-enhanced products that amplify creativity and interactivity across our platforms, Buzzfeed has been able to connect with the largest global audience of young people online to cement its role as the defining digital media company of the AI era. Notably, some of Buzzfeed's most successful tools and content experiences thrive on the power of small, focused datasets. Still wondering how Shrek fits into the picture? You'll have to watch!

Video from: https://smalldatasf.com/

📓 Resources Big Data is Dead: https://motherduck.com/blog/big-data-... Small Data Manifesto: https://motherduck.com/blog/small-dat... Why Small Data?: https://benn.substack.com/p/is-excel-... Small Data SF: https://www.smalldatasf.com/

➡️ Follow Us LinkedIn: / motherduck
X/Twitter : / motherduck
Bluesky: motherduck.com Blog: https://motherduck.com/blog/

Discover how BuzzFeed's Data team, led by Gilad Cohen, harnesses AI for creative purposes, leveraging large language models (LLMs) and generative image capabilities to enhance content creation. This video explores how machine learning teams build tools to create new interactive media experiences, focusing on augmenting creative workflows rather than replacing jobs, allowing readers to participate more deeply in the content they consume.

We dive into the core data science problem of understanding what a piece of content is about, a crucial step for improving content recommendation systems. Learn why traditional methods fall short and how the team is constantly seeking smaller, faster, and more performant models. This exploration covers the evolution from earlier architectures like DistilBERT to modern, more efficient approaches for better content representation, clustering, and user personalization.

A key technique explored is the use of text embeddings, which are dense, low-dimensional vector representations of data. This video provides an accessible explanation of embeddings as a form of compressed knowledge, showing how BuzzFeed creates a unique vector for each article. This allows for simple vector math to find semantically similar content, forming a foundational infrastructure for powerful ranking and recommender systems.

Explore how BuzzFeed leverages generative image capabilities to create new interactive formats. The journey began with Midjourney experiments and evolved to building custom tools by fine-tuning a Stable Diffusion XL model using LORA (Low-Rank Approximation). This advanced technique provides greater control over image output, enabling the rapid creation of viral AI generators that respond to trending topics and allow for massive user engagement.

Finally, see a practical application of machine learning for content optimization. BuzzFeed uses its vast historical dataset from Bayesian A/B testing to train a model that predicts headline performance. By generating multiple headline candidates with an LLM like Claude and running them through this predictive model, they can identify the winning headline. This showcases how to use unique, in-house data to build powerful tools that improve click-through rates and drive engagement, pointing to a significant transformation in how media is created and consumed.