Offline vs Online Machine Learning: Which is Better?
Offline vs Online Machine Learning: A Comparison of Training Methods
In, a ML pipeline after you chose ML Algorithm for the dataset. Next, comes how you would train the model in production. Learn incrementally from the stream of incoming data or otherwise. In this blog we will discuss ML Classifications based on Training in production. They are two training methods. They are:
OFFLINE Learning
ONLINE Learning
OFFLINE Learning
In offline learning, the dataset is static, and data is collected and prepared beforehand. The model is trained on batches of data which are split. Hence, it is also called Batch Learning. Model doesn't interact with the environment in real-time during training.
Example:
Imagine you're planning a big party and decide to bake cookies for your guests. You gather all the ingredients, carefully measure them out, and prepare the dough well in advance. On the day of the party, you bake all the cookies in one go, following the recipe exactly as you prepared it. You don't make any adjustments based on how the cookies are turning out during the baking process. This approach is much like offline learning in machine learning. Here, the dataset is collected and prepared beforehand, and the model is trained in one batch without making any real-time adjustments.
Advantages
Simplicity: Offline Learning is rather easier than online learning. We don't need to learn any new algorithms for handling real-time data.
Efficiency: Efficiency is higher than online learning because it processes the entire dataset at once.
Optimization: Optimization is also higher then online learning due to the fact that it processes entire dataset at once.
Disadvantages
Accuracy: The accuracy of the model will degrade over time if the data they encounter differs from the training data.
Storage: In offline learning the model is trained with the dataset in whole. It will be challenging to store very large datasets.
Adaptability: Offline models struggle with new data patterns.
Cost: It is costly because storing the data and training the model becomes costly over time.
ONLINE Learning
In contrast to offline learning, online learning models are updated on the fly. This makes them ideal for real-time data streams, adapting to changes in data patterns, and handling large datasets efficiently.
Example:
Imagine you're running a food truck that serves a variety of dishes based on customer preferences. Each day, you receive feedback from your customers about their favorite dishes and any suggestions they have. Instead of waiting until the end of the month to analyze all the feedback and make changes to your menu, you adjust your offerings daily based on the latest feedback. If a new trend emerges or a particular dish becomes popular, you can quickly adapt and add it to your menu. This approach is akin to online learning in machine learning, where the model is continuously updated with new data, allowing it to adapt to changes in real-time and handle large volumes of incoming information efficiently.
Advantages
Real-time updates: Models are updated with real-time data. They can adapt to the latest trends and patterns in the data, leading to accurate predictions.
Memory Efficiency: Online learning avoids the need to load all the data into memory at once because it processes the data incrementally, making it suitable for large-scale applications.
Scalability: Online learning algorithms can handle continuously growing datasets effectively.
Disadvantages
Convergence Challenges: With changing data, guaranteeing an optimal solution is a difficult task.
Forgetting Past Data: New data can disrupt learned patterns (mitigated by techniques like minibatch learning).
More Tuning: To achieve good performance, carefully tuning hyperparameters is essential.
BATCH VS ONLINE
Python Libraries for Online Learning
Scikit-Multiflow
Jubatus
River
Conclusion
In conclusion, both offline and online learning methods have their unique advantages and disadvantages, making them suitable for different applications. Offline learning is ideal for scenarios where data is static and computational resources are ample, while online learning excels in dynamic environments with real-time data streams. Understanding the specific needs of your project will help you choose the most appropriate approach for training your machine learning models.