jm + best-practices   14

Kubernetes Best Practices // Speaker Deck
A lot of these are general Docker/containerisation best practices, too.

(via Devops Weekly)
k8s  kubernetes  devops  ops  containers  docker  best-practices  tips  packaging 
july 2017 by jm
'Software Engineering at Google'
20 pages of Google's software dev practices, with emphasis on the build system (since it was written by the guy behind Blaze). Naturally, some don't make a whole lot of sense outside of Google, but still some good stuff here
development  engineering  google  papers  software  coding  best-practices 
february 2017 by jm
'Rules of Machine Learning: Best Practices for ML Engineering' from Martin Zinkevich
'This document is intended to help those with a basic knowledge of machine learning get the benefit of best practices in machine learning from around Google. It presents a style for machine learning, similar to the Google C++ Style Guide and other popular guides to practical programming. If you have taken a class in machine learning, or built or worked on a machine­-learned model, then you have the necessary background to read this document.'

Full of good tips, if you wind up using ML in a production service.
machine-learning  ml  google  production  coding  best-practices 
january 2017 by jm
fiunchinho/dockerize-me
'Tired of copy/pasting Dockerfiles around? Not sure about best practices for Dockerfiles or Docker entry points? This tool lets you Dockerize your applications using best practices to define your Dockerfile and Docker entry point files.'

The best practices in question are defined here: https://github.com/docker-library/official-images#review-guidelines
docker  dockerfile  images  build  best-practices  alpine  containers 
may 2016 by jm
Go best practices, six years in
from Peter Bourgon. Looks like a good list of what to do and what to avoid
go  golang  best-practices  coding  guidelines 
may 2016 by jm
Kafka best practices
This is the second part of our guide on streaming data and Apache Kafka. In part one I talked about the uses for real-time data streams and explained our idea of a stream data platform. The remainder of this guide will contain specific advice on how to go about building a stream data platform in your organization.


tl;dr: limit the number of Kafka clusters; use Avro.
architecture  kafka  storage  streaming  event-processing  avro  schema  confluent  best-practices  tips 
march 2015 by jm
Zeldman on Facebook's "Year In Review" feature
This is a great point.
When you put together teams of largely homogenous people of the same class and background, and pay them a lot of money, and when most of those people are under 30, it stands to reason that when someone in the room says, “Let’s do ‘your year in review, and front-load it with visuals,’” most folks in the room will imagine photos of skiing trips, parties, and awards shows— not photos of dead spouses, parents, and children.

So it comes back to this. When we talk about the need for diversity in tech, we’re not doing it because we like quota systems. Diverse backgrounds produce differing points of view. And those differences are needed if we are to put the flowering of internet genius to use actually helping humanity with its many terrifying and seemingly intractable problems.
best-practices  sensitivity  culture  design  silicon-valley  youth  privilege  facebook 
december 2014 by jm
Go: Best Practices for Production Environments
how Soundcloud deploy their Go services, after 2.5 years of Go in production
go  tips  deployment  best-practices  soundcloud  ops 
april 2014 by jm
Practical machine learning tricks from the KDD 2011 best industry paper
Wow, this is a fantastic paper. It's a Google paper on detecting scam/spam ads using machine learning -- but not just that, it's how to build out such a classifier to production scale, and make it operationally resilient, and, indeed, operable.

I've come across a few of these ideas before, and I'm happy to say I might have reinvented a few (particularly around the feature space), but all of them together make extremely good sense. If I wind up working on large-scale classification again, this is the first paper I'll go back to. Great info! (via Toby diPasquale.)
classification  via:codeslinger  training  machine-learning  google  ops  kdd  best-practices  anti-spam  classifiers  ensemble  map-reduce 
july 2012 by jm

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