Speaker
Abstract
The rapid advancement of artificial intelligence and machine learning technology has led to exponential growth in the open-source ML ecosystem. As a result, building a flexible ML platform that supports best practices and interoperability with diverse offerings has become increasingly crucial for enterprises seeking to scale ML-driven business impact.
In 2022, Spotify’s machine learning platform—in active use since 2018—adopted the name Hendrix as part of our multi-year journey to develop best-in-class infrastructure for powering our ML research, products, and experiments through the complete ML journey from prototype to production.
In this talk, Mike Seid and Divita Vohra will discuss Spotify’s newly branded platform and share insights gained from our five-year journey building ML infrastructure to empower over 600 internal ML practitioners in driving ML innovation for audio-lovers around the world. We will demonstrate how designing for augmentability and customizability as first-class experiences, creating user experiences that facilitate force-multiplying end-users' work, and establishing stable interfaces are critical components of building a robust ML platform. The larger MLOps community can learn from our extensive journey building and maintaining infrastructure for machine learning and benefit from the same increases in productivity and innovation that Spotify has witnessed.
Main Takeaways:
- Enterprise ML infrastructure should be an integrated set of products that cover common end-to-end use cases and are extensible for less common use-cases. An ML platform should embrace the best practices of augmentable systems and allow extension by its tenants or teams handling ML-adjacent workflows.
- The design of ML infrastructure should remain flexible to adapt to changing requirements and challenges. This flexibility enables platform builders to integrate with various tools and technologies in an ever-changing environment. Prioritizing flexibility in design enables organizations to ensure their systems remain scalable, efficient, and effective in achieving their business goals.
Interview
As the Product Area Tech Lead for the Machine Learning Platform, Mike’s day-to-day consists of defining the technical strategy and execution of delivery of the Product Area’s 50 person organization. Mike leads the engineering organization work to build the modern ML development experiences for Spotify Practitioners through a strong culture of collaboration, innovation and playfulness.
As a Senior Product Manager for Spotify’s ML workflows tooling and responsible ML efforts, Divita’s day-to-day is filled with discussions with ML practitioners, stakeholders in Trust & Safety, and strategic leaders to understand requirements and align ML infrastructure and responsible ML efforts with business goals. Conducting user and industry research to identify areas for improvement, emerging technologies, and regulatory requirements related to ML infrastructure are crucial tasks Divita engages in to ensure compliance while fostering a culture of learning and innovation in ML development across Spotify.
In our 5 year journey building an ML platform at scale, we’ve tackled appropriately standardizing on TFX components for TensorFlow-based pipelines, Kubeflow Pipelines for ML workflow orchestration, and recently, Ray for accelerated ML research and development. Along the way, we have overcome numerous challenges such as user migrations, multi-tenancy, resource management, cluster versioning, observability, and effective cost-tracking, all while keeping our infrastructure aligned with larger business goals. We believe that our experience in tackling these common challenges in ML infrastructure will be beneficial to other builders and maintainers in the field.
- Builders and maintainers of ML Infrastructure (Machine Learning Engineers, Product Managers, Data Engineers)
- ML Practitioners leveraging enterprise ML infrastructure (Data Scientist, Researchers, Data Engineers, Machine Learning Engineers)
Topics
QCon New York 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
MLOps: Navigating the Terrain of Large-Scale Models Hosted by Bozhao (Bo) Yu Founder @BentoML.aiFrom the same track
Wednesday 14 June
10:35 Salon D Session ML Infrastructure Introducing the Hendrix ML Platform: An Evolution of Spotify’s ML Infrastructure Divita Vohra, Mike Seid The rapid advancement of artificial intelligence and machine learning technology has led to exponential growth in the open-source ML ecosystem. 11:50 Salon D Session Machine Learning Improve Feature Freshness in Large Scale ML Data Processing Zhongliang Liang Engineering Manager @Facebook AI Infra 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. 13:40 Carroll Gardens Unconference Unconference: MLOps 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. 14:55 Salon D Session AI/ML A Bicycle for the (AI) Mind: GPT-4 + Tools Sherwin Wu, Atty Eleti OpenAI recently introduced GPT-3.5 Turbo and GPT-4, the latest in its series of language models that also power ChatGPT. 16:10 Salon D Session MLOps Platform and Features MLEs, a Scalable and Product-Centric Approach for High Performing Data Products Massimo Belloni Data Science Manager @Bumble 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. 17:25 Salon D Panel Panel: Navigating the Future: LLM in Production Sherwin Wu, Hien Luu, Rishab Ramanathan 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.