Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. Machine learning enables models to train on datasets 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.
Machine-learning techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data. In this section, we discuss the categories 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.
The advantage of machine learning is that it is possible to leverage algorithms and models to predict outcomes. The trick is to ensure that the data scientists doing the work area using the right algorithms, ingesting the most appropriate data (that is accurate and clean) and using the best performing models.
If all these elements come together it’s possible to continuously train the model and learn from the outcomes by learning from the data. The automation of this process of modelling, training the model and testing leads to accurate predictions to support business change.
The concept of machine learning essentially aims to make computers learn as humans do. Since its inception close to 50 years ago this technology has evolved, giving us better, more refined ways to find useful patterns in large amounts of data.
This is achieved by applying an algorithm which narrows down and specifies common ‘if-then’ programs resulting in more granular outcomes, widening the scope of its findings and creating more possible outcomes.
Machine Learning Platforms provide users with tools to build, deploy, and monitor machine learning algorithms. These platforms combine intelligent, decision-making algorithms with data, thereby enabling developers to create a business solution.
Machine Learning (ML) services help organizations to develop custom solutions based on proprietary or open-source algorithms/frameworks that process data and run sophisticated algorithms on cloud and edge. This ensures faster decision making, increased productivity, business process automation, and faster anomaly detection for the businesses.Contact US