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Online Vs. Offline Machine Learning

Machine learning models can be divided into two major parts on the basis of their training behavior and deployment, which are online and offline machine learning. It is very important to decide whether to go for an online machine-learning model or an offline machine-learning model before starting or developing the workflow of any data science project.

In this article, we will discuss the online and offline machine learning models, what they are, how they work, and the advantages and disadvantages associated with the same. This article will help one to understand the difference between online and offline models and will also help one to answer interview questions related to the same.

We will also discuss the hybrid models in machine learning (which are basically the combination of online and offline models) with their limitations and the case study of Netflix. 

So, let us start with the very simple and Important models, which are Offline Machine Learning models. 

Offline Machine Learning

In machine learning, generally, we have training data that is cleaned, preproc4essed and fed to the machine learning model, where the algorithms try to fit on the data and recognize the patterns and behavior of the data, and then once the model is trained, it is validated and tuned again if needed.

Offline machine learning or batch machine learning is a type of machine learning model development process where the model is trained on a fixed amount of data, meaning that the training data of the model is fixed and can not be changed during the whole process.

Now if we want to update the model later on to enhance the knowledge of the model, we have to retrain the complete model on the new data; we can not just easily insert or inject new data into the model.

Example: The spam filter machine learning model is a type of offline model that trains on a fixed amount of data. This model is not updated in real-time, and if there is a change needed in the model, then the model is taken out of the production stage and deployed again after retraining the same.

There are several advantages and disadvantages of this type of machine learning.

Advantages

1. Simplicity

Since the offline models are trained on a fixed amount of data, these types of models are very simple, and there are no complex steps and processes involved.

2. Stability

As we are training the model on a fixed amount of data, the model will be very stable until we retrain the model on new data. Meaning that the model stays very stable as it has a fixed amount of knowledge until the updation of the same.

3. Less Computations

Since we are training the model on a fix data, there is no real-time calculation involved for the model, and hence there is very less complexity involved with the offline type of models.

Disadvantages

1. Less Adaptability

The offline machine learning model is very less adaptable to the changes in environment or data; since we have to retrain the model to update its knowledge, it is very less adaptable to new changes compared to online models.

2. Cost of Retraining

If there is some change in the data, we need to retrain this type of model, which is sometimes a very costly process.

3. Cold Starting

The offline machine learning model is considered a cold starting model as they are initially trained on a limited amount of data, which may not be sufficient to have an accurate and reliable model. This problem can be solved by retraining the model on a sufficient amount of data.

Online Machine Learning

Online machine learning is a type of machine learning where the model is trained on a live feed of the dataset, meaning that here we have a live feed of the dataset available, and the model is constantly trained on the same.

This type of model is considered a very highly updated model, where the model is aware of all the sudden changes in the dataset and is prepared to have an effect of the same in the outputs.

Example: A recommendation system is an online type of machine learning model that is trained on a real-time dataset. Here the model collects the real-time data of the user activity and interaction on the web app or software, and according to that, the model recommends a similar type of thing to every individual user.

There are several advantages and disadvantages associated with online models.

Advantages

1. Good Adaptability

The online machine learning model is very adaptable compared to the offline model, as the model is trained on the live feed of the dataset.

2. Real-Time Outputs

As we are training the model on the real-time dataset, the model also generates the outputs in real-time, and the outputs are affected by the changes and events happening in real-time.

3. Reduced Resources

This type of model requires very fewer resources as they are trained on the live feed of the data, where the new data is coming, which is processed and fed to the model, unlike offline models where the whole model is retrained.

Disadvantages

1. Complex Models

This type of model is very complex to handle and understand. Since we are dealing with a real-time dataset, it is very hard sometimes to identify the error and mistakes in the models.

2. Data Preprocessing

Since we are dealing with the real-time dataset in online models, we need to first preprocess the data that is coming in real-time, and then only we can feed it to the model. Although the preprocessing of these data can sometimes be a very complex and time-consuming process, it may also require high costs.

3. Concept Drift

The real-time data that is coming may have one concept, but due to some event happening in real-time, the concept of the data may change, and hence the model need to adapt these changes very easily and efficiently in order to give accurate and reliable outputs.

Hybrid Models: The Combinations of Online and Offline Models

As we discussed earlier that online and offline models are good for specific scenarios and have their own advantages and disadvantages, which limits the use of the specific model in certain cases.

Although there is one more type of model, which is known as a hybrid model, basically the combination of online and offline models, which leverages the advantages and functionalities of both models.

In the hybrid model, initially, the offline machine learning models with certain data are trained, and then the model is deployed online where the continuous feed of training data is present on which the model gets trained over time.

These models are very advanced models which have large data handling capacity and perform very well in complex cases, combining the power of both online and offline models.

Although it also has some limitations, which sometimes limit the use of hybrid models, and one has to go with the single online or offline models.

Let us discuss some of the limitations of hybrid models.

Limitations of Hybrid Models

1. Increased complexity

As the hybrid models combine the online and offline machine learning approaches, the model complexity is very high in such cases, and it is also hard to interpret.

2. Increased Cost

As both the online and offline machine learning models are used here, the cost goes very high for such a model, which can be a major limitation for some smaller organizations and companies.

3. Overfitting Risk

As we are training the offline model first in the hybrid model, the model may not be able to capture all the patterns of the historical data, and then the model is shifted to the online mode, where the model may not be able to capture and recognize the patterns of real-time data, which can be resulste4d in overfitting, where model tries to memorize the training data, instead of learning the same.

Now let us discuss the case study where the hybrid models were used.

Hybrid Models: A Case Study

One of the popular implementations of the hybrid model is Netflix, and its backend system collects user data, and several things are recommended to the user.

Initially, the data of user preferences, likes, ratings, and survey results were collected on which the offline model was trained. After the successful training of the offline models, these models were deployed online, where the continuous real-time user interaction data was fed to the model, and the model became more and more accurate.

Now let us compare both types of machine learning models and conclude which is better.

Online Vs. Offline Learning – Which is Better?

Now as we have discussed both types of machine learning models with their advantages and disadvantages, a gentle question may appear in our mind, which is the performance, application, and reliability of the models in different scenarios.

We can not directly say that one of these models is always good and performs well than another in all cases. In fact, it totally depends upon the problem statement we are working on and the resources that we have.

The offline machine learning models can be a good choice when we do not have a live feed of the data, and the model is to be trained on a fixed amount of data. They can be a better choice when we are concerned about the cost of the model building and the complexity of the models.

On the other hand, the online machine learning models can be a great choice when we have a live feed of the data, and the cost of building a model is high enough to fulfill all model’s requirements and maintenance. This model can be a better choice in the case of concept drift and can adapt to the new concept in a short time without re-training the complete model.

Thus, the selection of which model to use is completely dependent upon the problem statement that we are working on and the resources we have to build the model. Hence this selection should be made very wisely, considering all the parameters and future scenarios.

Key Takeaways

1. The offline machine learning model trains on a fixed amount of data, where the model is updated by retraining the whole model on the new data.

2. An online machine learning model is the type of model that trains on the real-time feed of the dataset where the model is constantly updated.

3. Offline machine learning models are easy to understand and implement, whereas online models are complex to interpret and implement.

4. Offline machine learning models are not so adaptable to the changes in the dataset, whereas online models are so adaptable to the new changes in the dataset.

5. In the offline model, we do not get real-time predictions meaning that if there is a sudden change in data due to any event, we still get a late prediction or late effect of the same on the prediction of the model.

6. Hybrid models are the type of models that combines the power of online and offline models and can be used in very complex model-building processes.

7. The hybrid models are trained offline first on limited data, and then they are deployed online, where they train on real-time data.

8. The hybrid also has several advantages, majorly cost, and complexity, which sometimes limits the use of the same for smaller organizations.

Conclusion

In this article, we discussed the online and offline machine learning models, the core idea behind them, how they train, examples of the same, and the advantages and disadvantages associated with the same.

We also discussed the hybrid models, how they are trained, and what their limitations are with the case study of implementing the same.

The article concludes that every type of model in machine learning has its own advantages and limitations, which should be considered before training the model, and the best-fit solution should be selected according to the requirement from the model an the problem statement that is being worked on.

Parth Shukla
Parth Shukla
http://parthshukla.me
EnergyAI Engineer - A dynamic blend of an Energy Engineer by profession and an AI/ML wizard by passion.

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