Khái niệm cốt lõi
Creating an end-to-end machine learning project is crucial for success, from development to production.
Tóm tắt
This article discusses the importance of developing an end-to-end machine learning project, specifically focusing on creating a spam classifier. The content is structured as follows:
Establish a Data Science Project:
Develop a project following an end-to-end structure.
Spam Classifier Development:
Conduct EDA and Model Development.
Track experiments with MLFlow.
Model Deployment with FastAPI and Docker:
Create the back-end and front-end for the spam classifier.
Combine both using Docker Compose.
Data Drift Detection and Model Retraining Trigger:
Detect data drift using Evidently AI.
Implement a model retraining script.
Use Airflow for model retraining.
Conclusion: Summarizes the importance of end-to-end projects in machine learning.
The article emphasizes that successful machine learning projects go beyond development to include deployment and continuous value creation.
Thống kê
A machine learning project succeeds if the model is in production and creates continuous value for the business.
Many beginners focus only on data analysis and model development in data science and machine learning projects.
Creating an end-to-end machine learning project has become a necessity in today's landscape.