Curious to know how Adidas is transforming customer experience and business impact with agentic workflows, powered by Databricks? By leveraging cutting-edge tools like MosaicML’s deployment capabilities, Mosaic AI Gateway, and MLflow, Adidas built a scalable GenAI agentic infrastructure that delivers actionable insights from growing 2 million product reviews annually. With remarkable results: 60% latency reduction (15.5 seconds to 6 seconds) 91.67% cost savings (transitioning to more efficient LLMs) 98.5% token efficiency, reducing input tokens from 200k to just 3k 20% increase in productivity (faster time to insight) Empowering over 500 decision-makers across 150+ countries, this infrastructure is set to optimize products and services for Adidas’ 500 million members by 2025 while supporting dozens of upcoming AI-driven solutions. Join us to explore how Adidas turned agentic workflows infra into a strategic advantage using Databricks and learn how you can do the same!
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J
Speaker
Joana Ferreira
1
talks
ML Engineer
Adidas AG
I have worn many hats in the Machine Learning field, starting as a Data Scientist, transitioning to Data Engineering, and now specializing as a Machine Learning Engineer. I leverage this diverse background to develop advanced Machine Learning solutions that drive innovation and enhance efficiency across adidas. I am passionate about empowering teams to harness AI capabilities in an ethical and environmentally responsible way. By creating frameworks that align with this vision, I aim to foster a culture of Responsible AI.
Bio from: Data + AI Summit 2025
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