Life Cycle of Machine Learning
Machine Learning Life Cycle:
Machine Learning Life Cycle is basically a cycle of actions that need to be performed. It is a cyclic process to build an efficient machine-learning system. The main purpose of it is to find a solution to the problem. It involves mainly four major steps, which are given below:
1. Acquisition: You can acquire data from many sources, it might be data held by your organization or open data from the Internet. There might be one dataset, or there could be ten or more.
2. Prepare: You must come to accept that data will need to be cleaned and checked for quality before any processing can take place. These processes occur during the preparation phase.
3. Process: The processing phase is where the work gets done. The machine learning routines that you have created perform this phase.
4. Report: Finally, the results are presented. Reporting can happen in a variety of ways, such as reinvesting the data back into a data store or reporting the results as a spreadsheet or report.
Why machine learning is important nowadays?
The machine learning life cycle is very important because it describes the role of every person in a company in data science initiatives. It ranges from business to engineering personnel. It takes every project from inception to completion and gives a high-level perspective of how an entire data science project should be structured to result in real, practical business value.
Advantage of Machine Learning Life Cycle:
i. It provides the benefits of power.
ii. Machine Learning provides speed, efficiency, and intelligence.
iii. It also provides opportunities for improved performance, productivity, and robustness.