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Software engineering for machine learning: a case study

The Morning Paper

Software engineering for machine learning: a case study Amershi et al., Previously on The Morning Paper we’ve looked at the spread of machine learning through Facebook and Google and some of the lessons learned together with processes and tools to address the challenges arising. ICSE’19. Today it’s the turn of Microsoft.

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A Day in the Life of… a Software Training Specialist

Tasktop

Meet Jason Grodan, a Software Training Specialist at Tasktop! We spoke to Jason about the different training classes Tasktop offers, bouldering, and what it’s like to work from home. My role at Tasktop is a ‘Software Training Specialist’. We provide training for Customers and Partners as well as new employees.

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SKP's Java/Java EE Gotchas: Clash of the Titans, C++ vs. Java!

DZone

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. Most of us, as we spend years in our jobs — tend to be proficient in at least one of these. These include Python, PHP, Perl, and Ruby.

Java 207
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MLOps and DevOps: Why Data Makes It Different

O'Reilly

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. Software Development Layers. This approach is not novel.

DevOps 137
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Migrating a privacy-safe information extraction system to a Software 2.0 design

The Morning Paper

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 particular system discussed in this paper is Google’s email information extraction system. " Switching to a Software 2.0 In the Software 1.0

Systems 84
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Orchestrating Data/ML Workflows at Scale With Netflix Maestro

The Netflix TechBlog

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
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O’Reilly serverless survey 2019: Concerns, what works, and what to expect

O'Reilly

More than a fifth of the respondents work in the software industry—skewing results toward the concerns of software companies, and helping explain the preponderance of those with software engineering roles. As noted earlier, the majority of survey respondents are software engineers.