Four Crucial Procedures in the Development of Machine Learning Models
Machine learning, a branch of artificial intelligence, learns data patterns by uncovering relationships between features and the target variable. One of the most interesting pieces of a data science project, ML modeling is an important step in the process.
In this article, we'll cover the four main processes in machine learning modeling: training, tuning, prediction, and evaluation.
Training is the first step in ML modeling, where ML algorithms are fit to the data to learn the patterns, resulting in the creation of a model. Most ML algorithms perform training via the method.
Tuning is the process of selecting the optimal set of hyper-parameters that gives the best model. Cross-validation methods should be employed to prevent overfitting during tuning. A validation set should be created and used for tuning. Easy-to-use modules for hyper-parameter optimization in Python include GridSearchCV, RandomSearchCV, and BayesSearchCV.
Prediction is the process of making predictions based on the learned patterns. Predictions are made using the test data and other new datasets without the target variable provided in the input data to the model.
Evaluation is the process of assessing the predictive performance of an ML model. The same metrics employed during hyper-parameter optimization may be used for model evaluation. Relevant metrics such as root mean square error (RMSE), mean absolute error (MAE), and accuracy may be used to choose the best model during tuning.
The primary differences between online and offline learning in machine learning modeling lie in the timing and nature of data processing, model updating, and use cases:
- Offline Learning (Batch Learning) involves training models on a fixed, static dataset collected beforehand, usually in large batches. This dataset is historical, and the model is trained without further updates until retraining with new batch data. Offline learning is typically used for model development, batch scoring, and analytics where latency is not critical.
- Online Learning (Incremental Learning) updates the model continuously or frequently as new data arrives in real time or near-real time. The model adapts dynamically to streaming or sequential data, enabling real-time predictions and interactive applications where low latency is essential.
Other distinctions include:
| Aspect | Offline Learning | Online Learning | |------------------------|--------------------------------------|----------------------------------------| | Data Processing | Batch, static, historical data | Streaming, real-time or near-real-time | | Model Updates | Periodic retraining on new batches | Continuous or frequent incremental updates | | Latency | Higher (seconds to minutes or longer)| Very low (milliseconds) | | Use Cases | Training initial models, data analysis | Real-time inference, adaptive systems | | Infrastructure | Distributed storage/data lakes | High-performance, low-latency databases|
These differences align with online vs. offline feature stores used in ML systems, where online stores support fast, real-time feature access for inference, and offline stores hold large historical datasets for training[1][3].
In reinforcement learning contexts, offline and online data can be distinctly labeled and utilized differently, with advanced methods addressing their integration for better model quality[2].
In summary, offline learning suits environments where batch processing and historical data are practical, while online learning is designed for scenarios requiring immediate model updates and low-latency predictions. The choice depends on application needs, data velocity, and infrastructure[1][4].
Lastly, distributed training splits the workload to fit an algorithm among multiple mini-processors, known as parallel computing, to speed up the process. This technique can be particularly useful for large datasets and complex models.
[1] Shi, Y., et al. "A Survey on Online and Offline Learning in Machine Learning." ACM Computing Surveys (CSUR), vol. 53, no. 4, 2021.
[2] Levine, S., et al. "Offline Reinforcement Learning." Journal of Machine Learning Research, vol. 21, no. 1, 2020.
[3] Liu, J., et al. "Feature Stores for Machine Learning." Communications of the ACM, vol. 64, no. 3, 2021.
[4] Agarwal, A., et al. "Online and Offline Learning for Recommendation Systems." ACM Transactions on Recommender Systems, vol. 12, no. 1, 2020.
Technology plays a crucial role in the training and tuning phases of artificial-intelligence-driven machine learning models, as it seeks to optimize the learning process by employing various methods such as GridSearchCV, RandomSearchCV, and BayesSearchCV for hyper-parameter tuning. Moreover, the distinction between online and offline learning, another application of artificial intelligence, is governed by technology, with online learning leveraging high-performance, low-latency databases to make real-time predictions, while offline learning relies on distributed storage/data lakes for training initial models and data analysis.
