5 Principles Full Stack Developers and Solutions Architects Must Understand About Machine Learning

According to recent and separate studies from Gartner, Harvey Nash/KPMG, and O'Reilly, somewhere between 24% and 37% of organizations are at least moderately investing in machine learning and artificial intelligence. Much of this will be AI/ML embedded in applications like chatbots, recommendation engines, and virtual assistants and some consider RPAs a form of AI/ML. But it also means that more organizations are testing AI/ML on their proprietary data, developing models, and connecting models to their end user applications. The most advanced organizations using AI/ML like Twitter and Facebook are developing entire model development lifecycles to support ongoing model improvements are retraining. Integrating Applications with Machine Learning Models As a full stack developer or a solutions architect, it's quite likely that you'll be asked to integrate applications and data pipelines to ML models.