InfoQ

MAY 8, 2024

Jules Damji discusses which infrastructure should be used for distributed fine-tuning and training, how to scale ML workloads, how to accommodate large models, and how can CPUs and GPUs be utilized? By Jules Damji

Tuning 89

Tuning Infrastructure Artificial Intelligence Data Engineering 89

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. Auto Remediation generates recommendations by considering both performance (i.e., Multi-objective optimizations.

Tuning 210

Tuning Efficiency Big Data Engineering 210

Dynatrace

JULY 6, 2022

Automating IT practices without integrated AIOps presents several challenges. Expect to spend time fine-tuning automation scripts as you find the right balance between automated and manual processing. This requires significant data engineering efforts, as well as work to build machine-learning models.

Artificial Intelligence 189

Artificial Intelligence Tuning Strategy Big Data 189

InfoQ

APRIL 15, 2024

He specifically delved into Venice DB, the NoSQL data store used for feature persistence. The presenter shared the lessons learned from evolving and operating the platform, including cluster management and library versioning. By Rafal Gancarz

Artificial Intelligence 106

Artificial Intelligence Big Data Data Engineering Latency 106

The Netflix TechBlog

DECEMBER 21, 2020

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 203

Storage Latency Efficiency Data Engineering 203