Netflix通过战略合作提升会员体验，重点优化对话清晰度。从拍摄到播放，多个环节可能影响对话可理解性。Netflix利用行业标准音量和STOI指标，开发了对话清晰度测量系统。与Fraunhofer IDMT和Nugen Audio合作，推出了DialogCheck插件，帮助调音师实时调整对话清晰度，确保每句话都能被观众清晰听到。通过技术创新和行业协作，Netflix致力于为全球观众提供更沉浸、更易理解的视听体验。

Netflix构建了一个强大的广告事件处理平台，通过实时反馈优化广告投放。系统由客户端请求、服务器端广告插入、广告管理器和事件处理器组成，逐步引入持久化层和中心化事件收集系统，支持频率控制、计费和报告等核心功能。架构不断演进，以应对数据增长和新功能需求，确保广告投放的精准性和高效性。未来计划包括直播广告事件处理和增强数据信号，持续提升广告技术平台。

Netflix利用eBPF技术捕获TCP流日志，解决了IP地址与工作负载身份匹配的难题。通过FlowExporter和FlowCollector，Netflix实现了本地和远程IP地址的精确归属，避免了误归属问题。新方法采用时间范围和广播机制，确保流数据的准确性，提升了服务拓扑和网络健康的洞察力，为依赖审计和安全分析提供了可靠支持。

Netflix推出媒体制作套件（MPS），旨在通过云端技术简化全球影视制作流程。MPS整合了自动化工具，如素材上传、媒体库、远程工作站等，减少非创意性工作，提升效率。通过标准化和开放接口，MPS支持跨地区协作，降低技术门槛，让更多创作者专注于创意叙事。

Netflix开发了一个基于大语言模型（LLM）范式的推荐基础模型，旨在通过集中学习用户偏好，提升推荐系统的效率和创新性。模型采用自回归的下一项预测目标，并结合稀疏注意力和滑动窗口采样技术处理大规模用户交互数据。通过整合元数据和增量训练，模型能够应对新内容冷启动问题。该模型支持多种下游应用，如直接预测、生成嵌入和微调，显著提升了推荐质量和系统可扩展性。

Netflix通过引入“Title Health”端点，构建了全面的内容发布可观测性系统。系统标准化了通信协议，确保端点准确反映生产行为，并通过“Insight Triad API”快速定位和解决问题。实时数据通过Kafka队列处理，结合Hollow Feeds高效存储和分发。系统还具备“时间旅行”功能，模拟未来请求，提前发现并修复问题，确保内容发布的顺利推进。

Netflix通过追踪用户的“印象”数据，优化个性化推荐系统。利用Apache Flink和Kafka等工具，实时处理全球每秒百万级的印象事件，确保数据质量与高效分析。通过频率控制和历史记录，避免内容重复曝光，提升用户体验。未来将引入自动化性能调优和智能数据质量警报，进一步优化系统。

Building on the foundation laid in Part 1, where we explored the “what” behind the challenges of title launch observability at Netflix, this post shifts focus to the “how.” How do we ensure every title launches seamlessly and remains discoverable by the right audience?

In the dynamic world of technology, it’s tempting to leap into problem-solving mode. But the key to lasting success lies in taking a step back — understanding the broader context before diving into solutions. This thoughtful approach doesn’t just address immediate hurdles; it builds the resilience and scalability needed for the future. Let’s explore how this mindset drives results.

This article is the last in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Need to catch up? Check out Part 1, which detailed how we’re empowering Netflix to efficiently produce and effectively deliver high quality, actionable analytic insights across the company and Part 2, which stepped through a few exciting business applications for Analytics Engineering. This post will go into aspects of technical craft.

This article is the second in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Need to catch up? Check out Part 1. In this article, we highlight a few exciting analytic business applications, and in our final article we’ll go into aspects of the technical craft.

A month ago at QConSF, we showcased how Netflix utilizes Metaflow to power a diverse set of ML and AI use cases, managing thousands of unique Metaflow flows. This followed a previous blog on the same topic. Many of these projects are under constant development by dedicated teams with their own business goals and development best practices, such as the system that supports our content decision makers, or the system that ranks which language subtitles are most valuable for a specific piece of content.

As a central ML and AI platform team, our role is to empower our partner teams with tools that maximize their productivity and effectiveness, while adapting to their specific needs (not the other way around). This has been a guiding design principle with Metaflow since its inception.

At Netflix, we manage over a thousand global content launches each month, backed by billions of dollars in annual investment. Ensuring the success and discoverability of each title across our platform is a top priority, as we aim to connect every story with the right audience to delight our members. To achieve this, we are committed to building robust systems that deliver comprehensive observability, enabling us to take full accountability for every title on our service.

At Netflix, we use Amazon Web Services (AWS) for our cloud infrastructure needs, such as compute, storage, and networking to build and run the streaming platform that we love. Our ecosystem enables engineering teams to run applications and services at scale, utilizing a mix of open-source and proprietary solutions. In turn, our self-serve platforms allow teams to create and deploy, sometimes custom, workloads more efficiently. This diverse technological landscape generates extensive and rich data from various infrastructure entities, from which, data engineers and analysts collaborate to provide actionable insights to the engineering organization in a continuous feedback loop that ultimately enhances the business.

One crucial way in which we do this is through the democratization of highly curated data sources that sunshine usage and cost patterns across Netflix’s services and teams. The Data & Insights organization partners closely with our engineering teams to share key efficiency metrics, empowering internal stakeholders to make informed business decisions.

In our previous blog post, we introduced Netflix’s TimeSeries Abstraction, a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction. This counting service, built on top of the TimeSeries Abstraction, enables distributed counting at scale while maintaining similar low latency performance. As with all our abstractions, we use our Data Gateway Control Plane to shard, configure, and deploy this service globally.

Distributed counting is a challenging problem in computer science. In this blog post, we’ll explore the diverse counting requirements at Netflix, the challenges of achieving accurate counts in near real-time, and the rationale behind our chosen approach, including the necessary trade-offs.

Note: When it comes to distributed counters, terms such as ‘accurate’ or ‘precise’ should be taken with a grain of salt. In this context, they refer to a count very close to accurate, presented with minimal delays.

At Netflix, the Analytics and Developer Experience organization, part of the Data Platform, offers a product called Workbench. Workbench is a remote development workspace based on Titus that allows data practitioners to work with big data and machine learning use cases at scale. A common use case for Workbench is running JupyterLab Notebooks.

Recently, several users reported that their JupyterLab UI becomes slow and unresponsive when running certain notebooks. This document details the intriguing process of debugging this issue, all the way from the UI down to the Linux kernel.

As Netflix continues to expand and diversify into various sectors like Video on Demand and Gaming, the ability to ingest and store vast amounts of temporal data — often reaching petabytes — with millisecond access latency has become increasingly vital. In previous blog posts, we introduced the Key-Value Data Abstraction Layer and the Data Gateway Platform, both of which are integral to Netflix’s data architecture. The Key-Value Abstraction offers a flexible, scalable solution for storing and accessing structured key-value data, while the Data Gateway Platform provides essential infrastructure for protecting, configuring, and deploying the data tier.

Building on these foundational abstractions, we developed the TimeSeries Abstraction — a versatile and scalable solution designed to efficiently store and query large volumes of temporal event data with low millisecond latencies, all in a cost-effective manner across various use cases.