How to deliver on Machine Learning projects
So we had this idea of a new feature for our product. The only way to quickly do it was to somehow implement a machine learning algo and that would give us the result that we wanted. Viola!! It seemed simple.<p>Now our company doesn't have any machine learning expert or a data science genius. Going for hiring one would take time. Taking someone up on contract would be very expensive (our CEO wasn't ready to shell out that kinda money). So the task fell on me. They asked me to go through the multitudes of Machine leaning MOOCs out there and get a working prototype ready in 2 weeks.<p>I had already done Andrew Ng's course back when it came out for the first time. But my memory had faded for the lack of practice.<p>I re-ran the course again. I went over a couple of online ML books too.<p>Then I started thinking of the problem at hand. Unfortunately, it turned out to be a chicken and egg problem. For the feature to work perfectly we needed a large amount of training data to train our models. 
But without the feature actually deployed, we didn't have any way to collect any training data.<p>So we ultimately fell back to simple algo, that took it's decisions based on a few hard coded rules. Things have been working fine till now.