Speaker
Abstract
MLOps has become a major enabler to successfully operationalize ML applications and for ML practitioners to realize the power of ML to bring impact to business. The journey to implementing MLOps will be unique to each company. One common thread to successfully adopt MLOps is the need for a strategy and a set of principles.
At DoorDash, we’ve been applying MLOps for a couple of years to support a diverse set of ML use cases, such ETA predictions, the Dasher assignments, personalized recommendation of restaurants and menu items and more, and to perform large scale predictions at low latency.
This session will share our approach to MLOps, the strategy and principles that have helped us to scale and evolve our platform to support hundreds of models and billions of predictions per day, and deep dive on the technical aspects of scaling our feature store and prediction service.
Interview
I am currently leading the ML platform team at DoorDash. Our platform journey started a few years back and we are currently working on the second floor of our ML platform to support the various large and complex ML use cases that require distributed model training, model prediction flexibility and more
The use cases I mentioned are about recommendations, NLP, computer vision, and large language models.
At the previous QCon SF in 2022, one interesting thing I learned while attending the lightning talk given by Courtney Kissler, CTO @ Zuliy at the Women & Allies in Tech Breakfast was about using the criteria when making impactful decisions.
Topics
QCon London 2023 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Part of the track
Emerging AI and Machine Learning Trends Hosted by Mehrnoosh Sameki Principal PM Manager @MicrosoftFrom the same track
Wednesday 29 March
10:35 Mountbatten (6th Fl.) Session Machine Learning Strategy & Principles to Scale and Evolve MLOps @DoorDash Hien Luu Sr. Engineering Manager @Zoox & Author of MLOps with Ray, Speaker and Conference Committee Chair MLOps has become a major enabler to successfully operationalize ML applications and for ML practitioners to realize the power of ML to bring impact to business. The journey to implementing MLOps will be unique to each company. 11:50 Mountbatten (6th Fl.) Session Digital Twins Cognitive Digital Twins: A New Era of Intelligent Automation Yannis Georgas Intelligent Industry Lead @Capgemini Traditionally, Digital Twins have been helping businesses make data-driven decisions, increase efficiency, and improve the overall performance of their physical assets. 13:40 Mountbatten (6th Fl.) Session AI Responsible AI: From Principle to Practice! Mehrnoosh Sameki Principal PM Manager @Microsoft Enabling responsible development of artificial intelligent technologies is one of the major challenges we face as the field moves from research to practice. Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine… 14:55 Mountbatten (6th Fl.) Session Graphs Graph Learning at the Scale of Modern Data Warehouses Subramanya Dulloor Founding Engineer @Kumo.ai Data warehouses have become a staple for enterprises, providing a wealth of information that can be harnessed to improve decision-making through the use of machine learning (ML). 16:10 Mountbatten (6th Fl.) Session python Simplifying Real-Time ML Pipelines with Quix Streams: An Open Source Python Library for ML Engineers Tomáš Neubauer CTO & Co-Founder @Quix As data volume and velocity continue to increase, the need for real-time machine learning (ML) is becoming more pressing. However, building real-time ML pipelines can be complex and time-consuming, requiring expertise in both ML and streaming application development.