Explore a 23-minute conference talk from the Toronto Machine Learning Series (TMLS) that delves into the implementation of machine learning models in production at Loblaw Digital. Learn how ML models are crucial for various aspects of their e-commerce business, including product search, personalized shopping experiences, and order fulfillment. Discover the company's investment in building an ML observability stack within their ML platform to manage a high volume of models throughout their lifecycle. Gain insights into their centralized observability stack, which combines in-house inference logging with Snowplow analytics to capture user behavior data. Understand how these tools provide visibility into ML model influence on user interactions and revenue, enabling model improvements and driving real-world business outcomes. Consider future feature improvements and additions to the observability stack as discussed by Adhithya Ravichandran, Senior Engineer of ML Platform at Loblaw Digital.