High Scalability

MARCH 17, 2020

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. How to screen candidates efficiently, effectively, and without bias.

Education 105

Education Engineering Java Software Engineering 105

High Scalability

MARCH 3, 2020

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. How to screen candidates efficiently, effectively, and without bias.

Education 114

Education Engineering Games Java 114

High Scalability

FEBRUARY 18, 2020

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. How to screen candidates efficiently, effectively, and without bias.

Education 113

Education Engineering Games Java 113

High Scalability

FEBRUARY 9, 2020

Sisu Data is looking for machine learning engineers who are eager to deliver their features end-to-end, from Jupyter notebook to production, and provide actionable insights to businesses based on their first-party, streaming, and structured relational data. How to screen candidates efficiently, effectively, and without bias.

Education 108

Education Engineering Games Java 108

Adrian Cockcroft

FEBRUARY 13, 2023

Provides comparison of inference workload on P4dn GPU instances vs. AWS Trainium saving 92% energy and 90% cost, and training workload on P4dn vs. AWS Inferentia 2.6x shorter training time, saving 54% energy and 75% cost. Talk by the team that is actually working on reducing the carbon footprint of AWS.

AWS 64

AWS Energy Architecture Programming 64

All Things Distributed

SEPTEMBER 4, 2017

And thirdly, an "algorithmic revolution" has taken place, meaning it is now possible to train trillions of algorithms simultaneously, making the whole machine learning process much faster. For the past 20 years, thousands of software engineers at Amazon have been working on ML. Machine Leaning at Amazon.

Artificial Intelligence 147

Artificial Intelligence Transportation Education Airlines 147

O'Reilly

OCTOBER 19, 2021

As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. In effect, the engineer designs and builds the world wherein the software operates. What: The Modern Stack of ML Infrastructure.

DevOps 138

DevOps Software Engineering Infrastructure Open Source 138

The Netflix TechBlog

APRIL 26, 2022

We built Axion primarily to remove any training-serving skew and make offline experimentation faster. We make sure there is no training/serving skew by using the same data and the code for online and offline feature generation. Our machine learning models train on several weeks of data.

Storage 187

Storage Design Scalability Latency 187

O'Reilly

MAY 19, 2020

Live online training, videos, books, certification prep, and more, from O’Reilly and our partner publishers. O’Reilly Learning > We wanted to discover what our readers were doing with cloud, microservices, and other critical infrastructure and operations technologies. Respondent Demographics.

Cloud 141

Cloud Serverless AWS Architecture 141