Machine Learning Life Cycle

Introduction To Machine Learning Life Cycle

First of all, Machine Learning Life Cycle is an important processing part of the Machine Learning Concept. It is basically a cyclical process consisting of the various steps of Machine Learning model development. We all know that any computer system in the world has the capability to perform certain tasks automatically on its own without any human intervention. These automatic task performances are possible because of the presence of Machine Learning. Thus, these Machine Learning operations take place through certain steps of life cycles. These Life cycles are responsible for making Machine Learning a full-proof and sound concept. So, we can deduce that Machine Learning Life Cycles are indirectly responsible for the creation of state-of-the-art Machine Learning enabled projects.

Definition

Machine Learning is a branch of Artificial Intelligence that is being used to develop and create numerous types of software, desktop applications, web applications, cloud services platforms, and others. It works on a given set of data, sometimes in huge quantities, to interpret and analyze them and formulate feedback depending on those types of data. These operations and processes improve over time in accuracy and performance. The truth is we are surrounded by platforms based on Machine Learning. As a matter of fact, the everyday digital technologies and applications we use has been developed with the basic concepts of Machine Learning and Machine Learning Tools.

Phases Of Machine Learning Life Cycle

So now we know that the Machine Learning platform has been divided into major cyclical steps, each performing a very crucial & important set of tasks culminating into the overall significance and value of the Machine Learning Platform.

So, there are mainly seven major steps, the names of these are given below:

• Gathering Data
• Data Preparation
• Data Wrangling
• Analyse Data
• Train the Model
• Test the Model
• Deployment

These life cycle steps play a very vital role in the overall conduct of any operation or process from start to finish. These life cycles help us to understand and predict the various tasks and the purpose of these tasks. The power to contemplate and understand the need for such tasks is very crucial to achieve the best results and favorable feedback, and all of this is possible because of the Machine Learning Life Cycle steps.

Functions Of Each Life Cycle Phase:

Given below are the Machine Learning Life Cycle steps in detail:

Life Cycle Steps	Functions
Gathering Data	Identifying and gathering all the data-related problems.
Data Preparation	After collecting all the data, this step helps in categorizing the ordering of the data.
Data Wrangling	Selects, cleans, and modifies data into a much better form for further exploration.
Analyse Data	The function of this step is to analyze the data at hand to give it a more meaningful approach.
Train the Model	Training the data sets in the model to improve the performance of the model to produce better results.
Test the Model	This step includes the testing of the models to increase its accuracy.
Deployment	After passing all the tests in the previous life cycles, this last step helps in the deploying of these machine learning models.

So now we know the functioning and features of all the seven steps.

Importance Of Machine Learning

• The usage and practical applications of machine learning platforms are limitless due to the growing and ever-expanding need for industrial and corporate uses. Also, the mammoth increase in the availability of various types of data has also increased the size of the storage spaces for these data.
• Also, the constantly evolving and improving nature of Machine Learning makes it a potential and very reliable platform for many innovations and developments in the fields of digital technology and artificial intelligence.
• Machine Learning’s self-assessing and self-determining features of how to perform and when to perform a certain set of tasks make it a very important platform for the computer/digital technological world.

Conclusion

So now we know the importance of the Machine Learning Life Cycle and its significance in the overall performance of the Machine Learning platform. It plays a vital role in defining the overall functions and operations of the Machine Learning Platform and also its successes and achievements in this computer age.

Recommended Articles

This is a guide to Machine Learning Life Cycle. Here we discuss the introduction, importance, and phases, along with the functions of each life cycle phase. You may also have a look at the following articles to learn more–

1. What is Machine Learning?
2. Uses of Machine Learning
3. Introduction to Machine Learning
4. Machine Learning Features