Hello Data & AI London community, Merry Christmas and Happy new year to you all! We will be kicking off our first meet up of 2024 with a bang.... As the New Year resolutions kick in and dry Janners dry jan I thought March would be best to get the ball rolling with meet ups... and what a line up it is!

Enda Ridge \| Product Analytics and Google shopping @ Google Putting Algorithms into the Business - war stories and success stories Better Algorithms, Sophisticated Cloud Services, More Data. Improvements to the tools at our disposal continue at pace. Demands of the business for the latest innovations outpace our supply. We know that accelerating algorithm automation will decelerate business decline. We know that less time on manual process means more time on great user journeys and customer service. But what is simple in principle proves surprisingly difficult in practice.

In this talk, I will take us through what makes it hard to put data science and algorithms into action in a business. We’ll talk about the operational, people, process and technology blockers I’ve encountered and tips for how I (sometimes) overcame them.

About me Enda is the author of "Guerrilla Analytics – a practical approach to working with data". His teams have helped the world's largest Big Tech companies, FTSE 100 retailers and global consultancies maximise their cost savings and opportunities using data, analytics and machine learning. He currently leads the EMEA product analytics team in Google Shopping, helping understand how millions of merchants are receiving value from the billions of their offers on Google Search and Shopping using experimentation and data driven hypotheses. His PhD is in algorithm tuning. He is a bad runner, a good folk dancer and a great cat dad.

Hudson Mendes \| Ex Machine Learning Manager @ Peloton Challenges & Learnings when optimising a Classier for a Class-Imbalanced Multimodal Dataset Multimodal Deep Learning Classiers are known for being hard to optimise. They are computationally hungry and can't be trained on CPUs. Class-imbalanced datasets also force you out of the comfort of using Cross Entropy, which is convex, as your objective function. In this talk, we investigate aspects of a Multimodal Dataset (MELD) based on the Friends series as well as its separation challenges, the struggles and workarounds when training with PyTorch on TPUs and GPUs (over Google Colab), issues regarding the different scales of using embeddings from different modalities & the application of the Dice & Focal Losses as alternatives to Cross Entropy for class-imbalanced datasets.

Please aim to arrive at 6pm. The talks will start around 6.30pm (give or take a few mins) and there will be pizza and refreshments as always on arrival :)

Look forward to seeing you all soon!