What's included

• Data Exploration and Preprocessing

  - Conduct exploratory data analysis (EDA) to understand the characteristics and quality of the data - Identify and handle missing values, outliers, and data imbalances - Perform feature engineering and selection to derive relevant features for the model

• Model Architecture Selection and Experimentation

  - Discuss and evaluate different model architectures suitable for the given problem and data - Explore various algorithms and techniques - Experiment with different model configurations, hyperparameters, and ensemble methods

• Model Training and Evaluation

  - Prepare and preprocess data for model training - Train models on large-scale datasets using distributed computing resources - Evaluate model performance using appropriate metrics and cross-validation techniques

• Model Deployment and Monitoring

  - Package and containerize trained models for deployment - Deploy models to cloud or on-premises environments using scalable and fault-tolerant architectures - Implement monitoring and logging mechanisms to track model performance and detect drift

• Model Maintenance and Updates

  - Continuously monitor and analyze model performance in production - Retrain models with new data and update deployed models as needed - Implement model versioning and rollback strategies for seamless updates

• Documentation and Knowledge Transfer

  - Provide comprehensive documentation on data preprocessing, model architecture, training process, and deployment details - Conduct knowledge transfer sessions to ensure smooth handover and ongoing maintenance