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Steps to Create AI ML Solution
- This article provides a detailed roadmap for creating AI/ML solutions.
- Before gathering data, it is important to understand the problem and set success criteria for a successful model.
- Data can be collected from public datasets, web scraping, APIs, databases, and IoT devices.
- Data cleaning includes handling missing data using imputation or deletion, removing or fixing outliers, data transformation, and feature engineering.
- The data is then split into training, validation, and test sets.
- Based on the problem type, an appropriate machine learning algorithm is chosen, such as supervised learning, unsupervised learning, or reinforcement learning.
- The model is then trained, and hyperparameters are tuned using techniques like grid search and Bayesian optimization.
- The model is evaluated using appropriate metrics to assess its performance, such as accuracy, RMSE, or confusion matrix.
- Once the model performs well, it is deployed to a production environment using containerization and model serving tools.
- Finally, post-deployment monitoring and maintenance are done to ensure the system can handle real-world traffic and model drift.
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