Machine learning solutions in the real world are rarely just a matter of building and testing models. Managing and automating the lifecycle of machine learning models from training to optimization is, by far, the hardest problem to solve in machine learning solutions. To control the lifecycle of a model, data scientists need to be able to persist and query its state at scale. This problem might seem trivial until you consider that any average deep learning model can include hundreds of hidden layers and millions of interconnected nodes ;) Storing and accessing large computation graphs is far from trivial. Most of the times, data science teams spend a lot of time trying to adapt commodity NOSQL databases to machine learning models before arriving to the not-so-obvious conclusion: Machine learning solutions need a new type of database.