O'Reilly

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. The new category is often called MLOps. This approach is not novel.

DevOps 138

DevOps Software Engineering Infrastructure Open Source 138

The Netflix TechBlog

OCTOBER 18, 2022

These include ETL pipelines, ML model training workflows, batch jobs, etc. As Big data and ML became more prevalent and impactful, the scalability, reliability, and usability of the orchestrating ecosystem have increasingly become more important for our data scientists and the company.

Java 202

Java Scalability Traffic Architecture 202

The Netflix TechBlog

APRIL 26, 2022

We built Axion primarily to remove any training-serving skew and make offline experimentation faster. Figure 1: Netflix ML Architecture Fact: A fact is data about our members or videos. We make sure there is no training/serving skew by using the same data and the code for online and offline feature generation.

Storage 187

Storage Design Scalability Latency 187

The Netflix TechBlog

DECEMBER 3, 2019

mainly because of mundane reasons related to software engineering. On the other hand, very few data scientists feel strongly about the nature of the data warehouse, the compute platform that trains and scores their models, or the workflow scheduler. The steps can be arbitrary Python code.

Open Source 232

Open Source AWS Infrastructure Energy 232

The Netflix TechBlog

DECEMBER 3, 2019

mainly because of mundane reasons related to software engineering. On the other hand, very few data scientists feel strongly about the nature of the data warehouse, the compute platform that trains and scores their models, or the workflow scheduler. The steps can be arbitrary Python code.

Open Source 134

Open Source AWS Infrastructure Energy 134

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. Maestro is highly scalable and extensible to support existing and new use cases and offers enhanced usability to end users.

Processing 210

Processing Big Data Efficiency Engineering 210

O'Reilly

JULY 15, 2020

Software engineers comprise the survey audience’s single largest cluster, over one quarter (27%) of respondents (Figure 1). If you combine the different architectural roles—i.e., software and systems architects, technical leads—architects represent almost 28% of the sample. Respondent Demographics.

Database 135

Database Architecture Education Systems 135