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Intern – Automatic Hyperparameter Tuning for Demand Forecasting
4/5-month duration from February 2024
Mission/Challenges:
The intern will work on implementing a mechanism for automatic hyperparameter tuning in the AI Engine, which is responsible for ML-based demand forecasting. The goal is to make the engine autonomous and adaptive to each customer's needs by automatically setting hyperparameters. This will involve creating a system that uses techniques like holdout mode to calculate error metrics, avoid overfitting and underfitting problems, and adapt to new projects. Additionally, the intern will need to devise a strategy for the engine to monitor errors during weekly batches and adjust parameters, if necessary, limited to a set number of runs each week.
Skills and Requirements:
- Strong programming skills, preferably in Python.
- Knowledge of machine learning techniques, especially factorization machines.
- Understanding of hyperparameter tuning methods, such as grid search or Bayesian optimization.
- Familiarity with holdout validation techniques and error metric calculation.
- Ability to create scripts for automated parameter adjustments. Analytical thinking and problem-solving skills