LLMs in Production at GetYourGuide // Meghana Satish & Tina Treimane // LLMs III Talk

MLOps.community October 27, 2023
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The MLOps Community fills the swiftly growing need to share real-world Machine Learning Operations best practices from engineers in the field. While MLOps shares a lot of ground with DevOps, the differences are as big as the similarities. We needed a community laser-focused on solving the unique challenges we deal with every day building production AI/ML pipelines. We’re in this together. Come learn with us in a community open to everyone. Share knowledge. Ask questions. Get answers. Join us every week for a live virtual meetup, Wednesdays at 5 PM UK, 9 AM PT, where top experts share what they’ve learned running pipelines for organizations bigs and small. You can also check out our Slack that’s filled with tips and tricks to overcoming the common obstacles we’ve all hit in the real world. Find the solutions you need. Share, learn, and grow with us, as we work to bring standardization to the chaotic world of ML.

Video Description

// Abstract Discover how GetYourGuide navigates the dynamic landscape of LLMs and delivers products valuable to consumers and business. Will cover decision-making process of when to opt for LLMs over supervised models, offering practical insights into implementation and how these are put into production at GetYourGuide. On top of this will go deeper into strategic LLM prioritisation, streamlining their integration into product processes, and ensuring safe deployment to consumers. // Bio Meghana Satish Meghana Satish is currently working as an MLOps Engineer at GetYourGuide. She has previously held positions at Amazon AWS in Berlin and Microsoft IT in Hyderabad. In addition to her career in technology, Meghana is also a talented singer, dancer, and yoga practitioner. Tina Treimane Philosophy graduate with marketing experience turned Product Manager. My expertise is growth and data products and am passionate about solving business and customer problems using software. Care about topics like data-driven product development, building successful teams and mostly recently how we develop and deploy safely impacftul AI/ML products.

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