Machine Learning lifecycle:

The lifecycle of a machine learning project involves a series of steps that include: 



1.    Study the Problems: 

    The first step is to study the problem. This step involves understanding the business problem and defining the objectives of the model. 

2.    Data Collection: 

    When the problem is well-defined, we can collect the relevant data required for the model. The data could come from various sources such as databases, APIs, or web scraping. 

3.    Data Preparation: 

    When our problem-related data is collected. then it is a good idea to check the data properly and make it in the desired format so that it can be used by the model to find the hidden patterns. This can be done in the following steps: 

·        Data cleaning

·        Data Transformation

·        Explanatory Data Analysis and Feature Engineering

·        Split the dataset for training and testing. 

4.    Model Selection: 

    The next step is to select the appropriate machine learning algorithm that is suitable for our problem. This step requires knowledge of the strengths and weaknesses of different algorithms. Sometimes we use multiple models and compare their results and select the best model as per our requirements.

5.    Model building and Training: 

    After selecting the algorithm, we have to build the model. 

1.    In the case of traditional machine learning building mode is easy it is just a few hyperparameter tunings. 

2.    In the case of deep learning, we have to define layer-wise architecture along with input and output size, number of nodes in each layer, loss function, gradient descent optimizer, etc.

3.    After that model is trained using the preprocessed dataset.

6.    Model Evaluation: 

    Once the model is trained, it can be evaluated on the test dataset to determine its accuracy and performance using different techniques like classification report, F1 score, precision, recall, ROC Curve, Mean Square error, absolute error, etc. 

7.    Model Tuning: 

    Based on the evaluation results, the model may need to be tuned or optimized to improve its performance. This involves tweaking the hyperparameters of the model. 

8.    Deployment: 

    Once the model is trained and tuned, it can be deployed in a production environment to make predictions on new data. This step requires integrating the model into an existing software system or creating a new system for the model. 

9.    Monitoring and Maintenance: 

    Finally, it is essential to monitor the model’s performance in the production environment and perform maintenance tasks as required. This involves monitoring for data drift, retraining the model as needed, and updating the model as new data becomes available.