The Endeavour — The blog of John D. Cook

I help people make decisions in the face of uncertainty. Sounds interesting. I’m a data scientist. Not sure what that means, but it sounds cool. I study machine learning. I’m into big data. Even though each of these descriptions makes a different impression, they’re all essentially the same thing. There are distinctions. “Decision-making under uncertainty” emphasizes that you never have complete data, and yet you need to make decisions anyway. “Data science” stresses that there is more to the process of making inferences than what falls under the traditional heading of “statistics.” Despite the hype around the term data science, it’s growing on me. Machine learning, like decision theory, emphasizes the ultimate goal of doing something with data rather than creating an accurate model of the process that generates the data. “Big data” is a big can of worms. Bayesian statistics is much older than what is now sometimes called “classical” statistics.

Bartosz Milewski's Programming Cafe GigaSpaces Matt Jaynes, Founder of DevOps University, wrote a great post on Hacker News, What is Continuous Deployment, in which he points out one of challenges that many people gloss over when they embark on their DevOps journey. In a nutshell, Matt was saying that, "You should get your house in order before aspiring to do automation or CD." In other words, if your testing is not fully automated or is taking too long to process, then having continuous deployment may not be right for you. The thing that gets me so interested in this entire DevOps movement is that it forces you to think holistically about software development, business cycles and organizational culture. With regards to Jaynes’ post, even if you can build a car faster, lack of proper QA may lead to shipping defective parts, and you will still be shipping slowly. In short, you need to get your house in order before launching continuous deployment right into production. What is an Application-Centric Approach to DevOps? 1. 2. 3.

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