Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine learning model is the output generated when you train your machine learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model.
Machine learning enables models to train on data sets before being deployed. Some machine- learning models are online and continuous. This iterative process of online models leads to an improvement in the types of associations made between data elements. Due to their complexity and size, these patterns and associations could have easily been overlooked by human observation. After a model has been trained, it can be used in real time to learn from data. The improvements in accuracy are a result of the training process and automation that are part of machine learning.
Machine learning requires that the right set of data be applied to a learning process. An organization does not have to have big data to use machine-learning techniques; however, big data can help improve the accuracy of machine-learning models. With big data, it is now possible to virtualize data so it can be stored in the most efficient and cost effective manner, whether on-premises or in the cloud. In addition, improvements in network speed and reliability have removed other physical limitations associated with managing massive amounts of data at the acceptable speed. Add to this the impact of changes in the price and sophistication of computer memory and it’s now possible to imagine how companies can leverage data in ways that would have been inconceivable only five years ago.
