talk-data.com

Showcasing how analytics engineers can add value to a business by simplifying complex asks into an easy to analyze format. This particular example looks at how we can achieve great insight into our product's performance from a standardized table structure and some clever macros.

Speaker: George Apps, Analytics Engineering, Travelperk

Register for Coalesce at https://coalesce.getdbt.com

Please register through the following link:

• https://redis.com/events-and-webinars/driving-ai-innovation-with-redis-enterprise/

Date: Tuesday, October 24th Time: 08:00-10:00 Location: Redis Office, Floor 5 – Bridge House, London, SE1 9QQ Register to attend in-person or join through our live stream.

With the current attention on artificial intelligence, machine learning, and generative AI products like ChatGPT, companies are under a lot of pressure to develop applications with these technologies. But how do you turn terabytes of unstructured data – ranging from text to images, audio, and video – into useful information for your AI strategy or application?

The answer is a vector database - powered by a real-time data layer; a critical enabler in creating personalised\, blink of an eye experiences.

Join us for breakfast in our new office by Borough Market, or via live stream, as George von Bülow, Senior Solution Architect at Redis, shares how to use Redis’s vector database capabilities for real-time AI apps. We show the elements of building generative AI applications – using the Redis technology you already know.

Agenda: 08:00 - 8:30 Doors open, breakfast on arrival 08:30 - 09:30 Live stream/ In-person session begins: 'Driving AI Innovation: Making it easy to build Generative AI apps with Redis Enterprise' 09:30 - 10:00 Q&A, networking, wrap-up and close

Presenters: George von Bürlow, Senior Solution Architect at Redi Paul Ross, Partner Solution Architect at Redis

London Analytics Meetup #5 at Funding Circle's office in the City of London near Bank!

Please bring some ID.

Please register with first and last name so that we can match to the guest list on the day.

Please fill in this form so we can continue to keep this Meetup and it's topics relevant to you!

Please unRSVP if you find you can't make it nearer the time.

We'll have some food and drink courtesy of Funding Circle, who are our gracious hosts!

Talks:

Maiara Reinaldo - DE @ Funding Circle - "Self-serve data with dbt & DataHub"

George Apps - Staff BI Developer @ TravelPerk - "To Manage or not to Manage?"

Adam Timlett - Analytics Manager @ PPL - "How to Image Your Organisation's Dragon"

Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you’re already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work! About the Technology An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease. About the Book Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you’ll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you’ll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker. What's Inside Working with large, structured and unstructured datasets Visualization with Seaborn and Datashader Implementing your own algorithms Building distributed apps with Dask Distributed Packaging and deploying Dask apps About the Reader For data scientists and developers with experience using Python and the PyData stack. About the Author Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company. We interviewed Jesse as a part of our Six Questions series. Check it out here. Quotes The most comprehensive coverage of Dask to date, with real-world examples that made a difference in my daily work. - Al Krinker, United States Patent and Trademark Office An excellent alternative to PySpark for those who are not on a cloud platform. The author introduces Dask in a way that speaks directly to an analyst. - Jeremy Loscheider, Panera Bread A greatly paced introduction to Dask with real-world datasets. - George Thomas, R&D Architecture Manhattan Associates The ultimate resource to quickly get up and running with Dask and parallel processing in Python. - Gustavo Patino, Oakland University William Beaumont School of Medicine