High Scalability

APRIL 28, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). this is going to be a challenging journey for any backend engineer! T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Try out their platform. Apply here.

Education 102

Education Software Engineering Scalability Engineering 102

High Scalability

MARCH 30, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). this is going to be a challenging journey for any backend engineer! T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Try out their platform. Apply here.

Education 105

Education Software Engineering Engineering Big Data 105

High Scalability

MAY 26, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). this is going to be a challenging journey for any backend engineer! T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Try out their platform. Apply here.

Education 100

Education AWS Software Engineering Scalability 100

The Morning Paper

FEBRUARY 18, 2020

Many of the software engineering discipline and controls need to be brought over into an ML context. The following chart breaks down features in three main areas: training and auditing, serving and deployment, and data management, across six systems. But model inference migrating into the DBMS is a bolder prediction.

Government 95

Government Software Engineering Storage Database 95

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. Learn how to train interviewers so everyone's on the same page. Apply here.

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. Learn how to train interviewers so everyone's on the same page. Apply here.

Education 114

Education Games Engineering 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. Learn how to train interviewers so everyone's on the same page. Apply here.

Education 113

Education Games Engineering 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. Learn how to train interviewers so everyone's on the same page. Apply here.

Education 108

Education Games Engineering Java 108

O'Reilly

FEBRUARY 2, 2020

First, the behavior of an AI application depends on a model , which is built from source code and training data. You need a repository for models and for the training data. And that’s essential: if you need to understand how your model behaves, and you don’t have the training data, you’re sunk.

Software Architecture 68

Software Architecture Monitoring Software Engineering Architecture 68

The Morning Paper

FEBRUARY 16, 2020

In Software 1.0 But in Software 2.0 the majority of our effort goes into curating training data, i.e., specification-by-example of what the system should do. The biggest challenges we encountered during this migration were in generating and managing training data. In the Software 1.0 Managing training data.

Systems 84

Systems Design Software Software 84

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. Good discussion of the embodied carbon of silicon chip production.

AWS 64

AWS Energy Architecture Programming 64

The Morning Paper

JULY 7, 2019

Software engineering for machine learning: a case study Amershi et al., More specifically, we’ll be looking at the results of an internal study with over 500 participants designed to figure out how product development and software engineering is changing at Microsoft with the rise of AI and ML. ICSE’19.

Software Engineering 92

Software Engineering Engineering Software Software 92

SQL Server According to Bob

JUNE 9, 2017

The hotspot causes a convoy/train to occur on the page latch (UP) for the PFS page. Note: Adding multiple TEMPDB files resolves the train/convoy. SQL Server has sent the results to the client application and is performing final cleanup from the pervious query execution. Bob Dorr – Principal Software Engineer SQL Server.

Servers 40

Servers Software Engineering Engineering Software 40

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. Progress through machine learning.

Artificial Intelligence 147

Artificial Intelligence Transportation Education Airlines 147

Brendan Gregg

APRIL 30, 2018

Today's LISA attracts attendees working on all sizes of production systems, and its attendees include sysadmins, systems engineers, SREs, DevOps engineers, software engineers, IT managers, security engineers, network administrators, researchers, students, and more. Hope to see you in Nashville!

DevOps 43

DevOps Network Best Practices Programming 43

Tasktop

MAY 23, 2019

The beginning of my experience as a Junior Software Engineer on one of Tasktop’s ‘Integrations Teams’ marked a definitive transition in the way I learned and practiced computer science and software development. I began my co-op term at Tasktop with two weeks of boot camp-style training.

Software Engineering 40

Software Engineering Engineering Software Software 40

Brendan Gregg

APRIL 29, 2018

Today's LISA attracts attendees working on all sizes of production systems, and its attendees include sysadmins, systems engineers, SREs, DevOps engineers, software engineers, IT managers, security engineers, network administrators, researchers, students, and more. Hope to see you in Nashville!

DevOps 40

DevOps Network Best Practices Programming 40

The Agile Manager

JANUARY 31, 2014

On a person-by-person basis, it is faster, easier, and cheaper to hire, train, and staff industrial workers to work on a software development "factory floor" where they perform coding tasks in an assembly-line like way than it is to recruit, develop and mentor polyskilled software developers.

Software 73

Software Software Development Government 73

DZone

FEBRUARY 27, 2021

As a Software Engineer, the mind is trained to seek optimizations in every aspect of development and ooze out every bit of available CPU Resource to deliver a performing application. Recently, I spent some time checking on the Performance (not a very detailed study) of the various programming languages.

Java 207

Java C++ Benchmarking Programming 207

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

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

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

Dynatrace

JUNE 15, 2021

And the last sentence of the email was what made me want to share this story publicly, as it’s a testimonial to how modern software engineering and operations should make you feel. Our dynamic growth in engineering also made us invest in continuous training for new and existing hires.

AWS 190

AWS Traffic Architecture Azure 190

The Netflix TechBlog

OCTOBER 18, 2022

These include ETL pipelines, ML model training workflows, batch jobs, etc. Similarly, ML model training workflows usually consist of tens of thousands of training jobs within a single workflow. A large number of batch workflows run daily to serve various business needs.

Java 202

Java Scalability Traffic Architecture 202

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. Apache Iceberg Iceberg is a high-performance format for huge analytic tables.

Processing 210

Processing Big Data Efficiency Engineering 210

Abhishek Tiwari

JULY 20, 2019

For instance, if you are fast-growing VC funded e-commerce startup and your number one business priority is multiplying current growth and performing exceptionally well on key financial metrics charted out by your investors. Is it possible to draw inspiration from outside of software engineering? How is that even possible?

Engineering 61

Engineering Retail Airlines Healthcare 61

The Netflix TechBlog

OCTOBER 16, 2019

Gutenberg allows browsing older versions of data for use cases such as debugging, rapid mitigation of data related incidents, and re-training of machine-learning models. for example to train machine-learned models. a common use case when debugging and when training ML models. there is no dependency on previous versions of data.

Cache 187

Cache Architecture Metrics Java 187

O'Reilly

JULY 20, 2021

Most recently, IBM used Flickr photos that were shared under a Creative Commons license to train facial recognition algorithms. It comes down to a key question: is the risk associated with an action greater than the trust we have that the person performing the action is who they say they are? Source: [link].

Google 121

Google Games Technology Technology 121

O'Reilly

MAY 19, 2020

Live online training, videos, books, certification prep, and more, from O’Reilly and our partner publishers. More than one-third have adopted site reliability engineering (SRE); slightly less have developed production AI services. For this audience, SRE’s future is brighter than AI’s, however. Respondent Demographics.

Cloud 141

Cloud Serverless AWS Architecture 141

The Morning Paper

APRIL 16, 2019

Enabling students to build running performant versions of all of those systems in the time available is one challenge. We would like students to consider the performance characteristics of their systems, and our tests check that their designs attain reasonable run-time performance. Testing and grading their solutions is another!

Systems 43

Systems Efficiency Testing Strategy 43