Unless you're building something that is overly complex with a metric crap load of features, as a work around, checkout Jason Brownlee's book on implementing many of the scikitlearn algos in raw python. He breaks down how many of the algos work and breaks open the black box of scikitlearn. If you substitute pandas where necessary, you can streamline his code even more.
Really the amount of data you would be pickling from the scikitlearn object, on a trained model, is fairly minimal. Of course that changes depending on the number of features and the algo type. It's minimal enough that you could follow Jason's examples and load in the trained data you need via CSV and be ready to do some damage.
As more food for thought, there is a recent discussion on Quora from Quant Jesus Ernie Chan about machine learning algos, worth reading many times over. Unless of course it's a mirage and he's secretly converting his bank account into an extension of the Federal Reserve banking system with a XG Boost algo he's put together.