Despite the proliferation of open source tools like Databricks’ AutoML Toolkit, Salesforce’s TransfogrifAI, and IBM’s Watson Studio AutoAI, tuning machine learning algorithms at scale remains a challenge. Finding the right hyperparameters — variables in the algorithms that help control the overall model’s performance — often involves time-consuming ancillary tasks like job-scheduling and tracking parameters and their effects. That’s why scientists at LG’s Advanced AI division developed Auptimizer, an open source hyperparameter optimization framework intended to help with AI model tweaking and bookkeeping. It’s available from GitHub.
Source: Venture Beat
