Building Machine Training Models
Training a panda to jump might need 2 or 3 sessions before it learn to move from one bin to other with accuracy without falling. After this irrespective of shape, type or form of bin it can move using the skills it has learned. Machines on the other hand to learn something simple will need lot of data and scenarios exposure before they can predict the outcome with some accuracy. This is where machine training models play in .. For building machine models, we ran experiments using Amazon Sandbox and then trained using different ML models such as Sklearn, lgbm and gradient boosting algorithm - XGBoost. We used Model as service paradigm exposing the service using a docker container through Amazon ELB and orchestrated using Kubernetes (Rancher). Irrespective of what you are trying to achieve from the model there are six steps to build a machine learning model - Define Problem - Convert business problem that we are trying to solve to a machine learning problem. Understanding business