Machine Learning Engineer-AI support for ML engineers

Example

A user is working on a time series forecasting problem and is unsure whether to use ARIMA, Prophet, or a deep learning model like LSTM. The MLE evaluates the data characteristics, such as seasonality and trend, and suggests the most suitable models based on these features.

Scenario

In an e-commerce company, predicting future sales is crucial. The MLE assists the data science team in selecting the right model by analyzing historical sales data and providing a tailored recommendation, potentially combining ARIMA with a machine learning model for residual forecasting.

Example

A user is trying to improve the performance of a classification model but suspects that the current features are insufficient. The MLE advises on creating new features, such as interaction terms or polynomial features, to capture complex relationships within the data.

Scenario

In a credit scoring system, the MLE suggests creating new features from existing financial data, like calculating the ratio of debt to income or aggregating transaction amounts over different periods. These engineered features can significantly enhance the model's predictive power.

Example

A user is training a support vector machine (SVM) but is struggling to find the optimal hyperparameters, such as the regularization parameter (C) and the kernel type. The MLE guides the user through a grid search or random search process, explaining how to set up the search space and evaluate the results.

Scenario

In a healthcare application predicting patient outcomes, tuning hyperparameters of a model like SVM is critical to balancing accuracy and generalization. The MLE helps in systematically exploring different hyperparameter configurations to achieve the best model performance.