MLOps - Production & Delivery for ML Platforms

Machine learning operations (MLOps) is an emerging field that aims to address the challenges of deploying and managing machine learning models in production environments. It encompasses a wide range of practices and tools that facilitate the end-to-end machine learning lifecycle, from data preparation and model development to deployment, monitoring, and maintenance.

The MLOps landscape is rapidly evolving, driven by advances in machine learning algorithms, software engineering practices, and cloud computing infrastructure. In this conference, we will explore the latest trends, best practices, and case studies in MLOps, and discuss how they can be applied to improve the reliability, scalability, and efficiency of machine learning systems.
Topics covered in the track will include:

  • Data management and governance for machine learning
  • Model development and testing methodologies
  • Continuous integration and deployment for machine learning
  • Monitoring and alerting for machine learning systems
  • Automated model retraining and versioning
  • Collaboration and communication in MLOps teams
  • Ethics and fairness in machine learning operations

Whether you are a data scientist, machine learning engineer, or software developer this track will provide valuable insights and practical advice for building and managing machine learning systems at scale.

From this track

Session ML Infrastructure

Introducing the Hendrix ML Platform: An Evolution of Spotify’s ML Infrastructure

Wednesday Jun 14 / 10:35AM EDT

The rapid advancement of artificial intelligence and machine learning technology has led to exponential growth in the open-source ML ecosystem.

Divita Vohra

Senior Product Manager @Spotify

Mike Seid

Tech Lead for the ML Platform @Spotify

Session Machine Learning

Improve Feature Freshness in Large Scale ML Data Processing

Wednesday Jun 14 / 11:50AM EDT

In many ML use cases, model performance is highly dependent on the quality of the features they are trained and inference on. One of the important dimensions of feature quality is the freshness of the data.

Zhongliang Liang

Engineering Manager @Facebook AI Infra


Unconference: MLOps

Wednesday Jun 14 / 01:40PM EDT

What is an unconference? An unconference is a participant-driven meeting. Attendees come together, bringing their challenges and relying on the experience and know-how of their peers for solutions.

Session AI/ML

Building Production AI-Powered Applications with the OpenAI API and Plugins

Wednesday Jun 14 / 02:55PM EDT

We recently introduced Chat Completions into the OpenAI API – which currently powers the GPT-4 and ChatGPT APIs.

Sherwin Wu

Technical Staff @OpenAI

Atty Eleti

Software Engineer @OpenAI

Session MLOps

Platform and Features MLEs, a Scalable and Product-Centric Approach for High Performing Data Products

Wednesday Jun 14 / 04:10PM EDT

In this talk, we would go through the lessons learnt in the last couple of years around organising a Data Science Team and the Machine Learning Engineering efforts at Bumble Inc.

Massimo Belloni

Data Science Manager @Bumble


Panel: Navigating the Future: LLM in Production

Wednesday Jun 14 / 05:25PM EDT

Our panel is a conversation that aim to explore the practical and operational challenges of implementing LLMs in production. Each of our panelists will share their experiences and insights within their respective organizations.

Sherwin Wu

Technical Staff @OpenAI

Hien Luu

Sr. Engineering Manager @DoorDash


Wednesday Jun 14 / 10:30AM EDT



QCon New York 2023
June 13 - 15, 2023



Join us at QCon San Francisco on October 2-6, 2023 (In-person or Video-only pass)

Registration is now open!

Track Host

Bozhao (Bo) Yu


Bo is a seasoned founder with a wealth of experience in mobile consumer, gaming, and ML infrastructure. He founded BentoML, an ML platform that powers thousands of organizations across the globe in both the public and private sectors. Bo has also created the most engaged MLOps developers community, increasing active members by 4X within a year, as well as the world's first iPhone sports fan in-stadium app with more than 70% engagement rate.

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