The machine learning life cycle is a cyclic process to develop, train, and use machine learning models. It elucidates each step that an organization needs to follow to take advantage of machine learning techniques and use them in their business. In simple words, it defines an end-to-end process of solving a machine learning problem.
The machine learning life cycle is data-driven because the model and the output of training are linked to the data on which it was trained. Thus, the initial steps of the cycle exclusively explain how to deal with raw data. Then patterns are gathered from the data and finally used to predict an attribute or element. Let us understand each of these steps in detail.
PART 1: From data collection to exploratory data analysis
- Data collection– Gathering data is the first step of the machine learning life cycle. Data can be collected from various sources such as the internet and databases and can be stored in formats like CSV and XML. The quality and quantity of data determine the efficiency of the output obtained. The more the data available, the more accurate the prediction.
- Data exploration– The next step after data collection is data exploration. Looking for similarities in elements of the dataset, finding correlations, managing inconsistent and missing information that could skew the data findings later, all these are included under this step.
- Data wrangling- The collected raw data may have issues like missing values, duplicate or insignificant values, and noise. Dirty data can affect the accuracy of the predicted outcome. In order to fit in the machine learning model perfectly, the prepared data should be formatted and edited. Data wrangling is the process of cleaning complex datasets for easy access and analysis. It consists of the following processes- data cleaning, variable selection for the model, and transformation of data into a proper format for analysis.
- Data interpretation and analysis- After data cleaning, machine learning algorithms are used to build a suitable model. A large variety of machine learning models can be used. Some examples of models are regression models, clustering models, and reinforcement learning models. The machine learning models use various statistical and mathematical methods to analyze and visualize data and predict outcomes. Various statistical plots like histograms, pair plots, distribution plots, and heat maps are used to analyze and strike comparisons between the elements of the data.
PART 2: From model training to evaluation
- Train model – On completion of thorough data analysis, the entire data is split into training and testing sets. Training sets are used to fit and tune models. Training the model is required to understand patterns and trends from the cleaned data Machine learning algorithms use this training data to find model parameters like coefficients of polynomial and intercepts, and further use these to predict outcomes for test data.
- Test model – How are we tested when we attend university? – Through assessments and examinations. In machine learning as well, this needs to happen. Once the model is trained, it is tested. Testing the model determines the percentage accuracy depending on the problem being dealt with. The test data is assumed to be new data whose output values can be determined by the model’s algorithm. Predictions are gathered for the test dataset from the training model.
- Model deployment – Deployment of a machine learning model is the process of integrating a machine learning model into an existing production mechanism to make practical business decisions. If the resulting model produces accurate outputs efficiently, then it can be deployed to real-world systems.
Importance of the machine learning lifecycle
It is important because it describes the role of every person in a company who is dealing with data science initiatives and projects. It takes every project from beginning to completion and gives a high-level perspective of how the organization data should be structured and dealt with to obtain practical business value from it and leverage profits. If there exists an error in the execution of any one of the steps in the Machine Learning life cycle, the resulting model will not give accurate values and will not be of any practical use to organizations.
With Machine Learning gaining more traction in businesses and giving momentum to their increasing profit rates, a development lifecycle that supports learning models for building custom Machine Learning algorithms and applications has become very crucial.
Understanding every step of a machine learning life cycle and using it to select and use the most appropriate Machine Learning model is the ultimate aim of studying the Machine Learning cycle.