content: Ironic, that the blame for ending the sexiest job of the 21st century falls at the feet of the data scientists themselves. The tools, startups, and automation that we have been slaving away at are the same tools that will eventually, seemingly in the near term, make us irrelevant. The 4 steps of prediction: Predicting the future is complicated. People spend careers chasing "alpha" with stock modeling trying to outsmart the other bots in the wild. Despite the complexity, the prediction process can still be boiled down into 4 steps: Storage > Data Munging > Training > Prediction Storage: Storage can be saving your raw data in SQL or NOSQL data stores. This used to be hard. Setting up redundant storage, or building a Hadoop cluster was an ambitious undertaking. Now with Amazon's Elastic Map Reduce a 10yr old can do in less than an hour what use to take engineers weeks. Don't believe me? Click here. Data Munging: Depending on the type of data this can be one of the largest pain points in data science. Unstructured data is wrangled to be structured and missing values are dealt with. Most of the secret sauce I've seen in data science takes place in this step along with most of the work. Companies like Trifacta have sprung up to accelerate this process and with $41 million in funding I'd say they are adding value. Their current platform is not all-inclusive yet for different data types. Great, it can handle IP geolocation, or USER_AGENT digestion, but there is still so much more we need to automate. Training / Prediction: This part used to require a legit data scientist, and in some cases it still does for custom objectives or unusual problems. Many people in this space like to quote the "No free lunch theorem" to justify their needed talent. They say: "You can't automate this model selection step because there is no best model for all cases." It's post 2007... have you heard of cloud computing? Why not just try hundreds or thousands of models and use the best one? Now you have companies like Google and others offering cloud prediction services trying to eliminate the math entry level needed for prediction. You also have fantastic machine learning libraries to lean on such as scikit that make this as easy as it should be. Same principal applies, try a large list of models algorithmically, and spit out the best model object post parameter optimization. Every year this entire prediction pipeline becomes easier. Every year the barrier to entry goes down. What happens next: Data scientists will continue to suck expertise into generalized cloud tools. More data-centric startups will be funded with higher and higher seed rounds that enable more people to use deep learning, feature extraction, prediction, and achieve data insight. In the end, we will have a generalized data store, generalized data munger/feature extractor, and generalized prediction engine that relies on a generalized optimizer. No training required. Nirvana, a.k.a. Skynet. Eventually the entry level data scientist will go the way of the pre-radar listener, or the street lamp lighter. Thanks, but no thanks.