You are viewing content from a past/completed QCon -

Presentation: Automating Netflix ML Pipelines With Meson

Track: The Practice & Frontiers of AI

Location: Seacliff ABC

Day of week: Monday

Slides: Download Slides

Level: Intermediate

Persona: Architect, CTO/CIO/Leadership, Data Engineering, ML Engineer, Technical Engineering Manager

What You’ll Learn

  • What challenges you will face in orchestrating an ML based system
  • How to leverage multiple technologies to increase the effectiveness of your ML and Data Science efforts.


In this talk we discuss the evolution of ML automation at Netflix and how that lead us to build Meson, an orchestration system used for many of the personalization/recommendation algorithms. We will talk about challenges we faced, and what we learned automating thousands of ML pipelines with Meson.


What is your motivation for this talk?


We want to give a broader story on what it means to try to automate experience tests with machine learning; when you're dealing with tons of smart data scientists that want to try lots of things. How do you build the infrastructure that's lasting and provides value while you're getting this flood of new ideas and new technology that you need to support.


Who should come to your talk?


People who are familiar with the space or anyone curious about how we do orchestration at Netflix.


What can people come take away from this talk?


How you transition from the experimentation phase into the production phase. That's dealing with the issues of the day to day workflow of a data engineer. And that effort doesn't have necessarily an easily measurable metric like runtime or prediction accuracy. Those are the kinds of problems that we often have and in some ways they have a bigger impact on the ability to actually leverage ML at scale, not whether or not you can run a giant neural net.


What keeps you up at night?


Are there fundamental abstractions in how we think about modeling a pipeline? Because words like workflow and pipeline are sort of thrown around very interchangeably and I stay awake at night thinking about like where are the real seams here?

When you're interacting with an orchestration system like this, oftentimes so many things are sort of intertwined and braided together that you can't really tease out why it works the way it does. What are sort of the key pieces of how to actually construct these things especially in such an evolving space?

Speaker: Davis Shepherd

ML Management @Netflix

Davis Shepherd spent 4 years building re-enforcement learning systems before working at Netflix where he now develops their next generation of ML pipeline management software.

Find Davis Shepherd at

Speaker: Eugen Cepoi

Senior Software Engineer @Netflix

Eugen Cepoi has been working on data processing systems for general ETL like purposes and machine learning for the past 4 years. Before that he spent 3 years building web applications and infrastructure for them. He now works in the personalization infrastructure team at Netflix, where he focuses on developing the ML pipeline management software.

Find Eugen Cepoi at

Last Year's Tracks

  • Monday, 16 November

  • Remotely Productive: Remote Teams & Software

    More and more companies are moving to remote work. How do you build, work on, and lead teams remotely?

  • Operating Microservices

    Building and operating distributed systems is hard, and microservices are no different. Learn strategies for not just building a service but operating them at scale.

  • Distributed Systems for Developers

    Computer science in practice. An applied track that fuses together the human side of computer science with the technical choices that are made along the way

  • The Future of APIs

    Web-based API continue to evolve. The track provides the what, how, and why of future APIs, including GraphQL, Backend for Frontend, gRPC, & ReST

  • Resurgence of Functional Programming

    What was once a paradigm shift in how we thought of programming languages is now main stream in nearly all modern languages. Hear how software shops are infusing concepts like pure functions and immutablity into their architectures and design choices.

  • Social Responsibility: Implications of Building Modern Software

    Software has an ever increasing impact on individuals and society. Understanding these implications helps build software that works for all users

  • Tuesday, 17 November

  • Non-Technical Skills for Technical Folks

    To be an effective engineer, requires more than great coding skills. Learn the subtle arts of the tech lead, including empathy, communication, and organization.

  • Clientside: From WASM to Browser Applications

    Dive into some of the technologies that can be leveraged to ultimately deliver a more impactful interaction between the user and client.

  • Languages of Infra

    More than just Infrastructure as a Service, today we have libraries, languages, and platforms that help us define our infra. Languages of Infra explore languages and libraries being used today to build modern cloud native architectures.

  • Mechanical Sympathy: The Software/Hardware Divide

    Understanding the Hardware Makes You a Better Developer

  • Paths to Production: Deployment Pipelines as a Competitive Advantage

    Deployment pipelines allow us to push to production at ever increasing volume. Paths to production looks at how some of software's most well known shops continuous deliver code.

  • Java, The Platform

    Mobile, Micro, Modular: The platform continues to evolve and change. Discover how the platform continues to drive us forward.

  • Wednesday, 18 November

  • Security for Engineers

    How to build secure, yet usable, systems from the engineer's perspective.

  • Modern Data Engineering

    The innovations necessary to build towards a fully automated decentralized data warehouse.

  • Machine Learning for the Software Engineer

    AI and machine learning are more approachable than ever. Discover how ML, deep learning, and other modern approaches are being used in practice by Software Engineers.

  • Inclusion & Diversity in Tech

    The road map to an inclusive and diverse tech organization. *Diversity & Inclusion defined as the inclusion of all individuals in an within tech, regardless of gender, religion, ethnicity, race, age, sexual orientation, and physical or mental fitness.

  • Architectures You've Always Wondered About

    How do they do it? In QCon's marquee Architectures track, we learn what it takes to operate at large scale from well-known names in our industry. You will take away hard-earned architectural lessons on scalability, reliability, throughput, and performance.

  • Architecting for Confidence: Building Resilient Systems

    Your system will fail. Build systems with the confidence to know when they do and you won’t.