Deploying a Machine Learning Model
Machine Learning with Python
Course HomeLesson 1 - Linear Regression with Scikit LearnLesson 2 - Logistic Regression for ClassificationAssignment 1 - Train Your First ML ModelLesson 3 - Decision Trees and HyperparametersLesson 4 - Random Forests and EnsemblingAssignment 2 - Decision Trees and Random ForestsLesson 5 - Machine Learning Case StudyLesson 6 - Unsupervised Machine LearningGradient Boosting with XGBoostProject - Classical Machine Learning
Source Code: https://github.com/BirajCoder/practice-deployment/tree/main
Notebook: https://jovian.com/biraj/deploying-a-machine-learning-model
Model Deployment is a critical phase in the machine learning pipeline where a developed model is made available in a production environment, enabling it to generate real-world predictions. The value of machine learning can only be actualized when a model is successfully deployed and integrated into a product or service.
In this workshop, you'll delve into the process of deploying a machine learning model onto a web application using Flask, a leading Python web framework. By the end of the session, you'll have a firm grasp of the deployment process and be well-prepared to deploy your own models.
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