The podcast discusses improving infrastructure at Netflix and empowering data scientists, their operating approach and culture, the importance of machine learning models, the algorithms used in the Netflix app, video encoding versions and adaptive streaming, and personalization and collaboration in ML infrastructure.
Netflix leverages technologies like Metaflow and Titus to empower data scientists and improve collaboration.
Netflix is exploring the use of reinforcement learning to enhance the streaming quality for users by training machine learning models to determine the appropriate video bitrate based on real-time network conditions.
Deep dives
Machine Learning Infrastructure at Netflix
Machine learning infrastructure at Netflix is focused on scaling and making data scientists more productive. They leverage technologies like Metaflow and Titus to empower data scientists to build their own microservices and enable collaboration. The infrastructure includes tools for data preparation, feature engineering, training models, and orchestrating workflows. They also use EV cache for high-throughput, low-latency caching, and leverage AWS for their control plane. They are working on challenges such as improving the streaming experience, content exploration, and studio production. Feedback and a culture of learning are emphasized, enabling continuous improvement and innovation.
Machine Learning-Based Infrastructure for Video Bitrate
Netflix is exploring the use of reinforcement learning to improve video bitrate decisions. By training machine learning models to determine the appropriate video bitrate based on real-time network conditions, they aim to enhance the streaming quality for users. They use models hosted by a service that enables data scientists to define custom REST interfaces, fostering collaboration and making it easier to integrate models into business applications. They also leverage open-source projects like Metaflow and Titus for workflow orchestration and container-based infrastructure.
Challenges of Collaboration and Feedback in Data Science
One of the key challenges in machine learning infrastructure is enabling collaboration and effective feedback processes for data scientists. Netflix aims to bring software engineering best practices to the data science field, improving productivity and ensuring meaningful collaboration. Engaging in open communication, providing feedback, and creating a culture of learning are essential to help data scientists grow and improve their work. They emphasize psychological safety and a constant feedback loop, enabling everyone to give and receive feedback to enhance decision-making and problem-solving.
Hiring, firing, managing, and freedom and responsibility. Julie Pitt and Faisal Siddiqi discuss improving our infrastructure around new technologies and empowering data scientists to use their machine learnings models to make Netflix better every day.
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