Machine Learning Development Life Cycle

The machine learning development life cycle refers to the process of developing machine learning models and implementing them in a production environment. The life cycle typically involves the following steps:

1. Problem Understanding and Definition: The first step in the machine learning development life cycle and also known as Framing a machine learning (ML) problem. This is the most important phase of any ML Project. This phase is to clearly define the problem that the model will be solving. This involves understanding the business problem, identifying the data that will be used to train the model, and determining the desired outcome.
2. Data Collection, EDA, and Preprocessing: The next step is to collect and prepare the data that will be used to train the model. This may involve collecting data from various sources, understanding that data by doing EDA on the data, and then cleaning and preprocessing the data. Data collection can be done using APIs, Web scrapping, and Database-Warehousing. Some of the basic data preprocessing steps are handling duplicated data, handling missing data (null data), handling outliers, and scaling(standardization) the data.
3. Feature Engineering and Feature Selection:

• Feature engineering is the process of transforming raw data into a form that is more suitable for building a machine-learning model. This can involve creating new features by combining existing ones, scaling or normalizing variables, or encoding categorical variables. The goal of feature engineering is to create a set of features that are relevant to the problem at hand and that can help improve the performance of the model.
• Feature selection, on the other hand, is the process of identifying a subset of the most relevant features from the set of features created during feature engineering.

4. Model Selection and Training: Once the data is prepared, the next step is to select a machine learning algorithm and train the model on the data. This involves selecting the appropriate algorithm, tuning its hyperparameters, and evaluating its performance on the validation set.

5. Model Evaluation: After the model is trained, it must be evaluated on the test set to determine its performance. This may involve calculating performance metrics such as accuracy, precision, and recall.

6. Model Deployment: If the model performs well on the test set, it can be deployed in a production environment. This may involve integrating the model into an application or creating a service that can be accessed by other systems.

7. Model Maintenance: Once the model is deployed, it is important to monitor its performance and make updates as needed. This may involve retraining the model on new data or adjusting its hyperparameters.