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. 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

Dynatrace

JULY 6, 2022

Expect to spend time fine-tuning automation scripts as you find the right balance between automated and manual processing. AI that is based on machine learning needs to be trained. This requires significant data engineering efforts, as well as work to build machine-learning models.

Artificial Intelligence 182

Artificial Intelligence Tuning Strategy Big Data 182

The Netflix TechBlog

OCTOBER 18, 2022

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 202

Java Scalability Traffic Architecture 202

The Netflix TechBlog

NOVEMBER 20, 2023

IPS enables users to continue to use the data processing patterns with minimal changes. Introduction Netflix relies on data to power its business in all phases. As our business scales globally, the demand for data is growing and the needs for scalable low latency incremental processing begin to emerge. past 3 hours or 10 days).

Processing 210

Processing Big Data Efficiency Engineering 210

Abhishek Tiwari

JULY 20, 2019

Luckily, aircraft operating manuals and training procedures are so formalised and well established that there is no scope of performance degradation even if one or more crew members are replaced. Because you are changing team composition, you need robust norms of conduct and engineering practices in place.

Engineering 61

Engineering Retail Airlines Healthcare 61