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

Dynatrace

JULY 6, 2022

With ever-evolving infrastructure, services, and business objectives, IT teams can’t keep up with routine tasks that require human intervention. Expect to spend time fine-tuning automation scripts as you find the right balance between automated and manual processing. How organizations benefit from automating IT practices.

Artificial Intelligence 187

Artificial Intelligence Tuning Strategy Big Data 187

The Netflix TechBlog

MARCH 25, 2019

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can

Infrastructure 219

Infrastructure Big Data Transportation Architecture 219

Abhishek Tiwari

JULY 12, 2018

Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.

Latency 63

Latency Analytics Scalability Engineering 63

The Netflix TechBlog

MAY 26, 2020

Cloud Network Insight is a suite of solutions that provides both operational and analytical insight into the Cloud Network Infrastructure to address the identified problems. And in order to gain visibility into these logs, we need to somehow ingest and enrich this data. So how do we ingest all these s3 files?

Network 150

Network Tuning AWS Big Data 150

The Netflix TechBlog

DECEMBER 21, 2020

Some of the optimizations are prerequisites for a high-performance data warehouse. Sometimes Data Engineers write downstream ETLs on ingested data to optimize the data/metadata layouts to make other ETL processes cheaper and faster. Orient: Gather tuning parameters for a particular table that changed.

Storage 203

Storage Latency Efficiency Data Engineering 203