Summary

In this concluding chapter, our focus has been on the implementation aspects of Machine learning. We have understood what traditional analytics platforms have been and how they cannot fit the modern data requirements. You have also learned the architecture drivers that are promoting the new data architecture paradigms such as Lamda Architectures and polyglot persistence (multi-model database architecture), and how Semantic architectures help seamless data integration. With this chapter, you can assume that you are ready for implementing a Machine learning solution for any domain with an ability to not only identify what algorithms or models are to be applied to solve a learning problem, but also what platform solutions will address it in the best possible way.