The Netflix TechBlog

JANUARY 10, 2024

This architecture shift greatly reduced the processing latency and increased system resiliency. We expanded pipeline support to serve our studio/content-development use cases, which had different latency and resiliency requirements as compared to the traditional streaming use case. This drove the approach of the “release train”.

Processing 229

Processing Media Latency Innovation 229

The Netflix TechBlog

MARCH 4, 2024

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. We have also noted a great potential for further improvement by model tuning (see the section of Rollout in Production).

Tuning 210

Tuning Efficiency Big Data Engineering 210

All Things Distributed

NOVEMBER 29, 2017

Though the AWS Cloud gives you access to the storage and processing power required for ML, the process for building, training, and deploying ML models has unique challenges that often block successful use of this powerful new technology. The challenges begin with collecting, cleaning, and formatting training data.

Tuning 94

Tuning AWS Scalability Infrastructure 94

Dynatrace

DECEMBER 2, 2021

As organizations adopt microservices-based architecture , service-level objectives (SLOs) have become a vital way for teams to set specific, measurable targets that ensure users are receiving agreed-upon service levels. This trains your teams to be proactive in maintaining software quality and saves you money by avoiding downtime.

Metrics 196

Metrics Best Practices DevOps Infrastructure 196

The Netflix TechBlog

NOVEMBER 20, 2023

Whether in analyzing A/B tests, optimizing studio production, training algorithms, investing in content acquisition, detecting security breaches, or optimizing payments, well structured and accurate data is foundational. There are three common issues that the dataset owners usually face.

Processing 210

Processing Big Data Efficiency Engineering 210

The Morning Paper

JUNE 13, 2019

As we saw with the SOAP paper last time out, even with a fixed model variant and hardware there are a lot of different ways to map a training workload over the available hardware. The following figure highlights how just one of these variables, batch size, impacts throughput and latency on ResNet50.

Hardware 58

Hardware Latency Serverless Energy 58

IO River

NOVEMBER 15, 2023

Two of them are particularly gnarly: fine-tuning rules to perfection and managing a WAF over a multi-CDN architecture. Configuring and Maintaining WAF on a Multi-CDN‍Multi-CDN architectures, the double-edged swords. ‍Think of it as the captain of a well-trained squad, each member with a specialized skill set.

Traffic 52

Traffic Network Logistics Architecture 52