Data Collection: Gather, preprocess, and clean data from various sources to create datasets suitable for training machine learning models
Feature Engineering: Select, extract, and engineer relevant features from data to improve model performance
Model Selection: Choose appropriate machine learning algorithms and models based on the problem at hand, such as regression, classification, clustering, or deep learning models
Model Training: Train machine learning models using historical data and employ techniques like supervised, unsupervised, and reinforcement learning
Hyperparameter Tuning: Optimize model performance by adjusting hyperparameters and fine-tuning the model to achieve the best results
Evaluation: Assess model performance using various metrics and techniques, such as cross-validation, ROC curves, and confusion matrices
Deployment: Deploy machine learning models into production systems, including web applications, mobile apps, or IoT devices
Continuous Improvement: Monitor model performance in real-world applications and retrain models as necessary to adapt to changing data patterns

AI / ML Engineer

Job Description