知鸦日报2023-03-12

Our Data Science team developed a growth framework that helped to grow DAUs by 4x since 2019. Let’s explore the path that led us to that framework, aka The Growth Model!

Across the past couple of years, different mobile app teams across Lyft have been moving to Server Driven UI (SDUI) for three main reasons:

• To deal with business complexity
• To increase release velocity
• To be more flexible in how we staff and build features

This post is about Lyft Bikes and Scooters’ journey to SDUI, why we’ve gone down this path, and what’s worked well for us.

Learn how Shopify Data built new online inference capabilities into its Machine Learning Platform to deploy and serve models for real-time prediction at scale.

At Netflix, we test hundreds of different device types every day, ranging from streaming sticks to smart TVs, to ensure that new version releases of the Netflix SDK continue to provide the exceptional Netflix experience that our customers expect. We also collaborate with our Partners to integrate the Netflix SDK onto their upcoming new devices, such as TVs and set top boxes. This program, known as Partner Certification, is particularly important for the business because device expansion historically has been crucial for new Netflix subscription acquisitions. The Netflix Test Studio (NTS) platform was created to support Netflix SDK testing and Partner Certification by providing a consistent automation solution for both Netflix and Partner developers to deploy and execute tests on “Netflix Ready” devices.

Over the years, both Netflix SDK testing and Partner Certification have gradually transitioned upstream towards a shift-left testing strategy. This requires the automation infrastructure to support large-scale CI, which NTS was not originally designed for. NTS 2.0 addresses this very limitation of NTS, as it has been built by taking the learnings from NTS 1.0 to re-architect the system into a platform that significantly improves reliable device testing at scale while maintaining the NTS user experience.

Uber has multiple, domain-specific products to manage and distribute configuration changes at runtime across our many systems. These configuration products cater to different use cases: some have a web UI that can be used by non-engineers to change product configuration for different cities, and others expose a Git-based interface that primarily caters to engineers.

While these domain-specific configuration products have different applications, they share common parts that can be consolidated for simplicity and to reduce the overhead of operations, maintenance, and compliance. This article will cover how we consolidated and streamlined our underlying configuration and rollout mechanisms, including some of the interesting challenges we solved along the way, and the efficiencies we achieved by doing so.

If you have used the Myntra app to purchase a product, you would normally browse the product on the search page and select it to get the details. This action takes you to the product detail page where complete information about the product such as attributes, images, etc is shown.

Along with this information, you will also see whether that specific product can be delivered to the Pincode specified and what time it will take to get it delivered.

Grab has always regarded security as one of our top priorities; this is especially important for data platform teams. We need to control access to data and resources in order to protect our consumers and ensure compliance with various, continuously evolving security standards.

Additionally, we want to keep the process convenient, simple, and easily scalable for teams. However, as Grab continues to grow, we have more services and resources to manage and it becomes increasingly difficult to keep the process frictionless. That’s why we decided to move from Role-Based Access Control (RBAC) to Attribute-Based Access Control (ABAC) for our Kafka Control Plane (KCP).

In this article, you will learn how Grab’s streaming data platform team (Coban) deleted manual role and permission management of hundreds of roles and resources, and reduced operational overhead of requesting or approving permissions to zero by moving from RBAC to ABAC.

At Netflix, we built the asset management platform (AMP) as a centralized service to organize, store and discover the digital media assets created during the movie production. Studio applications use this service to store their media assets, which then goes through an asset cycle of schema validation, versioning, access control, sharing, triggering configured workflows like inspection, proxy generation etc. This platform has evolved from supporting studio applications to data science applications, machine-learning applications to discover the assets metadata, and build various data facts.

During this evolution, quite often we receive requests to update the existing assets metadata or add new metadata for the new features added. This pattern grows over time when we need to access and update the existing assets metadata. Hence we built the data pipeline that can be used to extract the existing assets metadata and process it specifically to each new use case. This framework allowed us to evolve and adapt the application to any unpredictable inevitable changes requested by our platform clients without any downtime. Production assets operations are performed in parallel with older data reprocessing without any service downtime. Some of the common supported data reprocessing use cases are listed below.

At Netflix, all of our digital media assets (images, videos, text, etc.) are stored in secure storage layers. We built an asset management platform (AMP), codenamed Amsterdam, in order to easily organize and manage the metadata, schema, relations and permissions of these assets. It is also responsible for asset discovery, validation, sharing, and for triggering workflows.

Amsterdam service utilizes various solutions such as Cassandra, Kafka, Zookeeper, EvCache etc. In this blog, we will be focusing on how we utilize Elasticsearch for indexing and search the assets.

基于 Inertia + Vue3 重构 ChatGPT 体验版前端并基于 Whisper 模型提供对语音聊天的支持。