The machine learning lifecycle is an approach for developing an effective ml project. It’s goal is to identify a solution to the problem or project.
The most critical aspect of the entire process is to realize where the problem is and know why it exists. As a consequence, before beginning the life cycle, we must first grasp the problem since a successful result is dependent on a deeper comprehension of the problem.
To address an issue throughout the whole life cycle process, you develop a machine learning technique called “model,” and this model is built by giving “training.”
There are seven major stages of a machine learning project.
In order to train a model, we need data, therefore the ml model development life cycle begins with data collection.
The initial phase in the machine learning life cycle is data collection. This step’s purpose is to discover and collect any data-related issues.
In this stage, we must identify the numerous data sources, as data may be obtained from a variety of sources, including files, databases, the internet, and mobile devices. It is one of the most crucial stages in the life cycle.
The quality and amount of the acquired data will affect the output’s efficiency. The more data science development life cycle there is, the more accurate the forecast.
This stage entails the following tasks:
- Determine multiple data sources
- Gather information
- Combine the information gathered from various sources.
By completing the preceding job, we have a cohesive set of data, also known as a dataset. It will be utilized in subsequent phases.
After gathering the data, we must prepare it for further processing. Data preparation is the process of putting our data in an appropriate location and preparing it for use in machine learning training.
In this stage, we first group all of the data and then randomize the order of the data.
This stage is further subdivided into two procedures:
- Data classification is used to understand the nature of the data with which we are working. We must comprehend data properties, format, and quality. A greater grasp of the data results in a more effective output. We detect correlations, general patterns, and outliers in this.
- The next step is to prepare the data for analysis – pre-processing of data.
The act of cleaning and turning raw data into a usable format is known as data wrangling. It is the process of cleaning the data, selecting the variable to utilize, and changing the data into a suitable format for analysis in the following phase. It is one of the most crucial phases in the entire procedure. To solve quality concerns, data must be cleaned.
It is not required that the data we have gathered be always valuable to us, as part of the data may not be. In real-world applications, acquired data may have a number of difficulties, such as:
- Values that are missing
- Data duplication
- Noise from invalid data
The detection and removal of the aforementioned difficulties are required since they might have a detrimental impact on the quality of machine learning in production.
The cleaned and prepared data is now forwarded to the analysis process. This stage entails:
- Analytical Techniques Selection
- Model construction
- Machine learning model monitoring
The goal of this stage is to create a machine learning model that will analyze the data using various analytical approaches and report on the results. It begins with determining the type of issue, after which we pick machine learning techniques such as classification, regression, cluster analysis, association, and so on, and then we create the model using prepared data and test it.
As a result, in this stage, you obtain the information and create the model using machine learning methods.
The following stage is to train the model; in this phase, we train our model to increase its performance in order to get a better solution to the problem.
To train the model, we employ datasets and several machine learning methods. A model must be trained in order for it to learn the numerous patterns, rules, and characteristics.
You test the ml model and in this stage, you verify your model’s accuracy.
The % correctness of the model is determined by testing it against the project or problem requirements.
The final stage of the machine learning model life cycle is deployment, in which we place the model in a real-world system.
If the above-prepared model produces an accurate output in accordance with our requirements at an acceptable speed, we install the model in the real system. However, before launching the project, we will examine whether or not it is increasing its performance using accessible data. The deployment phase is analogous to completing a project’s final report.