Do you want to start tackling Machine Learning Operations (MLOps) and know more about it? Or do you want to find more data engineering and MLOps solutions for your business needs? In this talk we will be dealing with MLOps principles and practices, lessons learned and the architectural solutions implemented in Trustpilot. In the last 3 years Machine Learning (ML) engineers, data scientists, and data engineers have joint efforts to bring Trustpilot to have a more robust and consistent use of artificial intelligence (AI) under MLOps principles. From identifying and defining MLOps gaps within the data science journey and listening to business needs, this joint collaboration effort resulted in the creation of the MLOps infrastructure within the Google Cloud Platform (GCP). As a result, the entire ML journey has been drastically improved, easing models prototyping, data retrieval processes and lowering models development and deployment time from months to weeks.

Monitoring embeddings on unstructured data is not an easy feat let's be honest. Most of us know what it is but don't understand it one hundred percent. Thanks to Aparna and Jason of Arize for breaking down embedding so clearly. At the end of this Lightning talk, we get to see a demo of how Arize deals with unstructured data and how you can use Arize to combat that.

We talk about forcing functions and how you can supercharge learning by putting yourself into a situation where you have a responsibility to others to learn.

In this Lighting Talk, Peeyush Agarwal explains 2 key pieces of the ML infrastructure at Chime. Peeyush goes into detail about the current feature store design and feature monitoring process along with the ML monitoring setup. This Lighting Talk is brought to you by arize.com reach out to them for all of your ML monitoring needs.

Can AI be truly creative? Can model ML models augment human creativity? How do we leverage ML for creating works of Art, Music, and even Jokes? This presentation answers these questions and more by carefully looking at various open-source ML Models and understanding their strengths, drawbacks, and societal implications. It also includes live demos with code walkthroughs of creative projects using open-source MusicVAE, GAN Art, Neural StyleTransfer, SketchRNN, StableDiffusion, Open AI Whisper, and transformer-based models for joke generation.

The Lisboa MLOps Community fires up another Meetup in Lisbon on the 27th of October. After taking inspiration from prior roundtables from Deep Learning Sessions Portugal, they discussed multiple subjects within MLOps in a roundtable fashion, along with their primary focus: ML Platform thinking.

If you're not an avid gamer yourself, you have never really seen how ML might be used in the gaming space. It's so interesting to see the things that are different like full stories of players' games from start to finish.

The relationship between ML Engineers and Product Managers is something that we don't talk about enough. We've got to get this right. If we don't get this right, either you're not focusing on the business problems in the right way or the Product Managers are not going to understand the tech appropriately to help make the right decisions.