Reducing size of Docker images

Making efficient final images

Most blogs and manuals will recommend you the simpler approaches to reducing the image of your docker image. We’ll go a little further today but let’s reiterate them anyway:

• Use the reduced version of base images (alpine usually recommended), avoid SDKs for final images
• Use multistage build, do not copy over temporary files or sources
• Take care of the .dockerignore, ignore as much as possible

Having said that, it is possible that you’ll still end up with a very huge docker image, and it’s difficult to understand what the next step from here.

This is where this post comes in.

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Working effectively with legacy code (review)

Your weapons against any badly designed code

I just finished reading Working effectively with legacy code, by Michael Feathers. The title is perfectly descriptive and quite ambiguous at the same time. Let me explain to you why.

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My sh*tpost generator: screaming-bot

Text generation with nltk, markovify, Tumblr, docker

Text generation with nltk, markovify, Tumblr, docker

(Image used without permission from Gunshow comic: Robot that screams.)

If the word offends you, below the fold I use it a lot, so you might not want to read this article. However, I think it’s the most appropriate term.

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Head First Data Analysis

Introductory solid book on Data Analysis

I just finished reading “Head First Data Analysis “, by Michael Milton, and it was the first book I’ve ever read from the “Head First” series.

The book was very easily approachable, with concepts introduced only when they were necessary and to make a good, valid, practical point. The whole structure of the book revolves around practice and “real life” examples (although, greatly simplified) to prove how methodical logical steps can naturally lead to a good analysis mechanism.

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Deep Learning (book review)

by Goodfellow, Bengio and Courville

I finally finished reading Deep Learning, by Ian Goodfellow, Yoshua Bengio and Aaron Courville. And as a wonderful as a book as it is, it wasn’t an easy read, at all.

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Preventing an AI arms race: open research

Secrecy is the halt of humanity's progress

I’ve noticed a trend in the AI research field, and one I feel pretty proud to talk about.

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Ciencia de datos ágil

Curso de Ernesto Mislej

(Post only in Spanish since I’m reviewing Spanish content. It’s a Udemy course talking about Data Science in an agile framework.)

Hace unos días terminé de participar del curso Ciencia de datos ágil, por Ernesto Mislej, co-fundador de 7puentes. Al comienzo me recomendaron el curso porque cubría un hueco que no estaba muy bien explicado en las fuentes online: cómo llevar adelante un proyecto de Data Science, fuera del típico proceso waterfall que siempre se describe. Además, Ernesto es una fuente de buena reputación, por lo que el curso me interesó.

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A Novice's Introduction to Data Science

Guest post on Making Sense's blog

I just wrote a guest post at Making Sense’s blog: A Novice’s Introduction to Data Science. Hopefully the first in a series, but for the moment, feel free to check that one out to find out what Data Science is and why you all the hype about it lately.

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How Google’s New AI Innovations Will Transform Retail

by Rae Steinbach, in cooperation with Y Media Labs

This is another great guest post, this time from Rae Steinbach. She is a graduate of Tufts University with a combined International Relations and Chinese degree. After spending time living and working abroad in China, she returned to NYC to pursue her career and continue curating quality content. Rae is passionate about travel, food, and writing, of course.

Her post talks about the impact that Google’s AI vision will have on retail businesses. Thanks Rae; thank you very much!

During 2017’s Google I/O, where developers from all around the world explore emerging technologies together, the company announced several new elements for both Google Home and Google Assistant. For instance, Google Assistant, now equipped with AI, will be able to provide relevant information about your environment by “seeing” it through the phone’s camera. You could just point the lens at a business you pass on the street, suddenly receiving information about its services, customer reviews, and more.

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The future of work, aided by AI?

How machine learning algorithms may take on our work

I recently came across the article Using Artificial Intelligence to Augment Human Intelligence, by Shan Carter and Michael Nielsen. I’d like to tell you a bit about the ideas that this essay mentions, and a few interpretations of my own about them.

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