This follow-up to How to Win at Machine Learning fills in some details on various stages of the process that you may experience as your first Machine Learning project unfolds. Our advice is to use supervised or unsupervised techniques, and follow our iterative Extract / Explore / Transform / Model flow.Read More
How do you build a fast, reliable Elasticsearch cluster? Use a recent version. Get your topology basics right: 2n+1 master nodes. Use standalone, master, data, query and ingest nodes appropriately. Use available RAM wisely. Disable or minimize swapping. Use an appropriate number of shards (hint: 1 is probably a good default).
We have a new tool to run (most of) these checks for you: www.elasticsearchmadeeasy.comRead More
What does any CTO, tech architect, or product owner need to know in order to make their first machine learning project a success? Realistic expectations, a sound and methodical approach, enough time and resources to complete the project, plus some key skills in your team.Read More
We take a look at Elasticsearch, how it started, what people have been doing with it, what makes Elasticsearch great, and what it can do for you.Read More
This is the first article in a series, describing an opt-in method to add parallel execution to an existing CLJS test suite. This article explains the general approach for adding parallelism with a minimum of fuss to our Clojurescript tests, takes a tour of cljs.test internals, and digs into some of the key abstractions we will extend in future articles to complete our "holy grail" of parallelized CLJS unit tests.Read More
Once upon a time, it was important to have developers, designers, and database administrators all in one place. That time is no more. ¡VIVA LA REMOTE REVOLUCION!Read More