There's a key statement here that most will either miss or never reach. The second last paragraph
> Machine Learning Automation
Tuning models is a pain, and if you don't understand the model and all its parameters (like most software engineers who's job is the write software and not build models!) it takes time and can be immensely frustrating. Randal Olson[1] just announced TPOT[2], a Python tool that "automatically creates and optimized machine learning pipelines using genetic programming". This is going to be a huge lever for engineers wanting to experiment/implement with ML algorithms.
> Machine Learning Automation
Tuning models is a pain, and if you don't understand the model and all its parameters (like most software engineers who's job is the write software and not build models!) it takes time and can be immensely frustrating. Randal Olson[1] just announced TPOT[2], a Python tool that "automatically creates and optimized machine learning pipelines using genetic programming". This is going to be a huge lever for engineers wanting to experiment/implement with ML algorithms.
[1] http://www.randalolson.com/2016/05/08/tpot-a-python-tool-for... [2] https://github.com/rhiever/tpot