Which is the most suitable pre-trained machine learning model for NYC taxi trip duration prediction dataset, full scale MLOPS production grade project?

For the New York City Taxi Trip Duration prediction task, selecting the most suitable pre-trained machine learning model depends on the nature of the dataset and the features involved. Since this is a regression problem where the goal is to predict the duration of taxi trips based on factors like distance, traffic conditions, and weather, here are a few approaches with suitable models:

1. Gradient Boosting Models

• Pre-trained models like LightGBM or XGBoost can be highly effective for structured/tabular data, such as the NYC Taxi dataset. These models perform well on regression tasks and often outperform deep learning models on structured datasets. You can fine-tune these models on the NYC dataset to optimize them for this specific problem.

• Why suitable?

  • Works well with tabular data.
  • Handles non-linear relationships and interactions between features.
  • Pre-trained on a general dataset can be easily fine-tuned.

2. Deep Learning Models

• TabNet or TabTransformer: These deep learning models are designed for tabular data and may outperform traditional machine learning models in cases where feature interactions are complex. You can use pre-trained versions of these models and fine-tune them on your dataset.

• Why suitable?

  • Can capture complex patterns in large-scale tabular data.
  • Beneficial for high-dimensional data with many interactions between features.

3. Time Series Models

• Temporal Fusion Transformers (TFT): While the NYC Taxi dataset is not a traditional time series dataset, certain models designed for time series forecasting can be adapted for regression tasks with a temporal aspect (e.g., time of day, day of week). TFT, for example, could be useful for capturing patterns based on time-related features.

• Why suitable?

  • Designed to model time-based relationships.
  • Can incorporate static, known, and future time-varying covariates.

4. Neural Networks with Pre-trained Embeddings

• Wide & Deep models: These are hybrid models combining neural networks and linear models, pre-trained on large datasets, and can be fine-tuned for structured regression tasks. They can capture the interactions between categorical and continuous variables efficiently.

• Why suitable?

  • Performs well on structured data with both categorical and continuous features.
  • Easy to fine-tune on a variety of tasks.

Best Option for NYC Taxi Dataset:

• XGBoost or LightGBM are typically the most suitable pre-trained models for this type of dataset due to their performance with tabular data and ease of integration into MLOps pipelines. These models are highly interpretable, scalable, and provide strong baseline results for regression problems.

• TabNet is also a viable choice if you want to leverage deep learning for more complex feature interactions, especially in a production-grade MLOps setting.