25 Steps for Machine Learning Model Deployment
Deploying a machine learning model transforms a trained algorithm into a real-world usable application. Below are 25 essential steps for final year students in Bangalore:
Understand the problem statement and use case
Collect and prepare the dataset
Perform data cleaning and preprocessing
Apply feature engineering techniques
Split data into training and testing sets
Select the appropriate ML algorithm
Train the machine learning model
Evaluate model performance using metrics
Optimize and fine-tune the model
Save the trained model using serialization
Create a REST API using Flask or FastAPI
Load the trained model into the API
Handle input validation and preprocessing
Implement real-time prediction logic
Test the API locally
Create a simple frontend or API client
Containerize the application using Docker
Deploy the model on cloud platforms
Configure environment variables and security
Monitor model performance and accuracy
Handle errors and exceptions gracefully
Log predictions and system activity
Optimize deployment for scalability
Prepare deployment documentation
Create final project report and PPT
These steps help students build production-ready ML systems suitable for academic evaluation and industry use.
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