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Foundation Model for Personalized Recommendation

The Netflix TechBlog

Yet, many are confined to a brief temporal window due to constraints in serving latency or training costs. The impetus for constructing a foundational recommendation model is based on the paradigm shift in natural language processing (NLP) to large language models (LLMs).

Tuning 179
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Netflix at AWS re:Invent 2019

The Netflix TechBlog

Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. Netflix runs dozens of stateful services on AWS under strict sub-millisecond tail-latency requirements, which brings unique challenges. Wednesday?—?December

AWS 38
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Migrating Critical Traffic At Scale with No Downtime?—?Part 2

The Netflix TechBlog

Behind these perfect moments of entertainment is a complex mechanism, with numerous gears and cogs working in harmony. Replay traffic testing gives us the initial foundation of validation, but as our migration process unfolds, we are met with the need for a carefully controlled migration process.

Traffic 285
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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

In addition to Spark, we want to support last-mile data processing in Python, addressing use cases such as feature transformations, batch inference, and training. We use metaflow.Table to resolve all input shards which are distributed to Metaflow tasks which are responsible for processing terabytes of data collectively.

Systems 235
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Telltale: Netflix Application Monitoring Simplified

The Netflix TechBlog

For example, a latency increase is less critical than error rate increase and some error codes are less critical than others. This simplifies the post-incident review process for many teams. A healthy Netflix service enables us to entertain the world. Client metrics and QoE changes. Alerts triggered by our alerting platform.

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Growth Engineering at Netflix?—?Automated Imagery Generation

The Netflix TechBlog

entertainment?—?and Server-generated assets, since client-side generation would require the retrieval of many individual images, which would increase latency and time-to-render. To reduce latency, assets should be generated in an offline fashion and not in real time. the background image shown above).

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Netflix at AWS re:Invent 2019

The Netflix TechBlog

Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. Netflix runs dozens of stateful services on AWS under strict sub-millisecond tail-latency requirements, which brings unique challenges. Wednesday?—?December

AWS 100