8.1/n Continuing here the twitter summary of my keynote talk ("Best practices of using machine learning in businesses") at the @BudapestBI data science conference. Tip #7: Do *online* validation of ML models (on live data, using the same pipeline as you would use in production) 8.2/n Besides the evaluation you do when you develop/train a model (evaluation on separate test/hold-out dataset / using cross validation) you must also do evaluation of the online model (deployed model/scoring on live data and using the same data pipeline as used in production)

3.1/n Continuing here the twitter summary of my keynote talk ("Best practices of using machine learning in businesses") at the @BudapestBI data science conference. Tip #2: Use open source (machine learning tools) 3.2/n A majority of people use R or python for data science nowadays. One can access most of the best open source machine learning libraries from R and python. Open source is great not only because it's free, but...

1.1/n Today I was the keynote at the @BudapestBI machine learning, R, pydata and datavis confererence. I'm summarizing my talk here in a twitter thread. Because of the format (keynote) and audience (all attendees/tracks in a plenary session) this was a... 1.2/n ... higher-level talk compared to the more technical talks I usually give. I tried to give a few tips, "best practices" for doing machine learning in practice/businesses. I aimed to cover 10 tips (in 45 mins), though of course there are many more tips I could give.