Machine Learning Application Using POSITRON: A Case Study of Backend vs Frontend

You should be expecting the concluding part of this training which we intend to start on 26th July, 2024 and continue until 27th July, 2024. The summary of the training expectation is given below:

Demonstrate Integration: Illustrate how Positron can be used to develop and integrate machine learning models within both backend and frontend environments.
Model Training and Evaluation: Train a machine learning model on a dummy dataset using Positron, and evaluate its performance to ensure it meets the necessary criteria.
Model Deployment: Deploy the trained machine learning model using a Flask web application, showcasing how Positron facilitates seamless model deployment.
API Development: Develop a RESTful API using Flask that allows users to interact with the trained machine learning model for predictions.
Frontend Interaction: Create a simple web interface that interacts with the backend API, demonstrating how machine learning predictions can be made accessible through a web application.
Performance Analysis: Compare the performance of machine learning applications in backend and frontend environments, highlighting any differences in response time and accuracy.
Error Handling: Implement robust error handling and debugging techniques using Positron to ensure the reliability of the machine learning application.
User Experience: Evaluate the impact of using Positron on the overall user experience for developers and end-users, focusing on ease of development and accessibility of machine learning models.
Workflow Optimization: Identify best practices for optimizing the development workflow with Positron, particularly in the context of machine learning projects that span both backend and frontend development.
Scalability and Maintenance: Examine the scalability and maintenance aspects of the machine learning application developed using Positron, ensuring it can handle increased load and be easily updated.