The complexity and quality trade-off
Building robust machine learning models requires substantial computational resources to process the features and labels. Coding a complex model requires significant effort from data scientists and software engineers. Complex models can require substantial computing power to execute and can take longer to derive a usable result.
This represents a trade-off for businesses. They can choose a faster response but a potentially less accurate outcome. Or they can accept a slower response but receive a more accurate result from the model. But these compromises aren’t all bad news. The decision of whether to go for a higher cost and more accurate model over a faster response comes down to the use case.
For example, making recommendations to shoppers on a retail shopping site requires real-time responses, but can accept some unpredictability in the result. On the other hand, a stock trading system requires a more robust result. So, a model that uses more data and performs more computations is likely to deliver a better outcome when a real-time result is not needed.
As Machine Learning as a Service (MLaaS) offerings enter the market, the complexity and quality of trade-offs will get greater attention. Researchers from the University of Chicago looked at the effectiveness of MLaaS and found that “they can achieve results comparable to standalone classifiers if they have sufficient insight into key decisions like classifiers and feature selection”.
Poor Quality of Data
Data plays a significant role in the machine learning process. One of the significant issues that machine learning professionals face is the absence of good quality data. Unclean and noisy data can make the whole process extremely exhausting. We don’t want our algorithm to make inaccurate or faulty predictions. Hence the quality of data is essential to enhance the output. Therefore, we need to ensure that the process of data preprocessing which includes removing outliers, filtering missing values, and removing unwanted features, is done with the utmost level of perfection.
Underfitting of Training Data
Let’s say for a child, to make him learn what an apple is, all it takes for you to point to an apple and say apple repeatedly. Now the child can recognize all sorts of apples.
Well, machine learning is still not up to that level yet; it takes a lot of data for most of the algorithms to function properly. For a simple task, it needs thousands of examples to make something out of it, and for advanced tasks like image or speech recognition, it may need lakhs(millions) of examples.
Overfitting of Training Data
Overfitting refers to a machine learning model trained with a massive amount of data that negatively affect its performance. It is like trying to fit in Oversized jeans. Unfortunately, this is one of the significant issues faced by machine learning professionals. This means that the algorithm is trained with noisy and biased data, which will affect its overall performance. Let’s understand this with the help of an example. Let’s consider a model trained to differentiate between a cat, a rabbit, a dog, and a tiger. The training data contains 1000 cats, 1000 dogs, 1000 tigers, and 4000 Rabbits. Then there is a considerable probability that it will identify the cat as a rabbit. In this example, we had a vast amount of data, but it was biased; hence the prediction was negatively affected.
Changing expectations and concept drift
Machine learning models operate within specific contexts. For example, ML models that power recommendation engines for retailers operate at a specific time when customers are looking at certain products. However, customer needs change over time, and that means the ML model can drift away from what it was designed to deliver.
Models can decay for a number of reasons. Drift can occur when new data is introduced to the model. This is called data drift. It can also occur when our interpretation of the data changes. This is concept drift.
To accommodate this drift, you need a model that continuously updates and improves itself using data that comes in. That means you need to keep checking the model.
That requires the collection of features and labels and to react to changes so the model can be updated and retrained. While some aspects of the retraining can be conducted automatically, some human intervention is needed. It’s critical to recognise that the deployment of a machine learning tool is not a one-off activity.
Machine learning tools require regular review and update to remain relevant and continue to deliver value.
Monitoring and maintenance
Creating a model is easy. Building a model can be automatic. However, maintaining and updating the models requires a plan and resources.
Machine learning models are part of a longer pipeline that starts with the features that are used to train the model. Then there is the model itself, which is a piece of software that can require modification and updates. That model requires labels so that the results of an input can be recognised and used by the model. And there may be a disconnect between the model and the final signal in a system.
In many cases when an unexpected outcome is delivered, it’s not the machine learning that has broken down but some other part of the chain. For example, a recommendation engine may have offered a product to a customer, but sometimes the connection between the sales system and the recommendation could be broken, and it takes time to find the bug. In this case, it would be hard to tell the model if the recommendation was successful. Troubleshooting issues like this can be quite labour intensive.
Machine learning offers significant benefits to businesses. The ability to predict future outcomes to anticipate and influence customer behaviour and to support business operations are substantial. However, ML also brings challenges to businesses. By recognising these challenges and developing strategies to address them, companies can ensure they are prepared and equipped to handle them and get the most out of machine learning technology.
This is one of the common issues faced by machine learning professionals. The machine learning models are highly efficient in providing accurate results, but it takes a tremendous amount of time. Slow programs, data overload, and excessive requirements usually take a lot of time to provide accurate results. Further, it requires constant monitoring and maintenance to deliver the best output.