Advantages of Machine Learning | Disadvantages of Machine Learning

Machine Learning as a field has seen a rapid increase in popularity and interest over the last several years. There is no single field in the modern world that ML has not affected. As a result, ML Engineers are in great demand. It not only offers a cushy career, but also the opportunity to solve real-world issues and effect tangible change.

Just as every coin has two sides, machine learning also has its set of pros and cons. In this article we will take a deeper look at advantages and disadvantages of machine learning.

Advantages of Machine Learning

1. Trend identification

Machine Learning can analyse huge amounts of data and identify particular trends and patterns not immediately visible to the human eye.
For example, Netflix or Amazon Prime use machine learning techniques to understand their users’ browsing habits and watch histories in order to provide them with personalized recommendations.

2. Continual learning

A machine learning model is only as accurate as the data it is provided with. As algorithms acquire experience, their accuracy and efficiency improve.
As the amount of data grows, algorithms learn to generate more accurate predictions in a shorter amount of time.

3. Data management

Machine Learning algorithms excel at dealing with multi-dimensional and multi-variety data. They can do so in dynamic (eg: data does not follow a specific format) or unpredictable situations.

4. Automation

Machine learning does not need constant human attention or supervision. A programmer would only need to set up the model to be trained and provide data for training testing and validation. At worst, intervention would involve diagnosing and remedying errors. This is to say, once the model is set up, there is no need for human intervention.

5. Applicability

ML has the unique advantage of being able to be applied in almost any field. Once applied, it makes everything easier, from identifying the target user base to providing intensive reports, a seamless workflow can be created.

Examples of applications:

Disease diagnosis
CCTV event detection
Tweet sentiment analysis

Disadvantages of Machine Learning

1. Data Acquisition

Machine learning models use a lot of data for training and testing. This necessitates large data sets to train on, which must be comprehensive, impartial, and of high quality. This can occasionally result in data inconsistencies. Because some data is regularly updated we’ll have to wait for new data to arrive. If this is not the case, the old and new data may yield different findings.

Datasets might also include a considerable amount of false or otherwise incorrect information which affects accuracy

2. Resource demand

ML requires adequate time for the algorithms to learn and mature to the point where they can serve their goal with a high level of accuracy and relevance. The more data there is, the longer it takes to learn from it and process it. It also requires a lot of resources to run. This may result in the utilisation of greater CPU power in some cases, which is why GPUs are recommended. But even with GPUs, things may become a little crazy. Furthermore, the data may consume more than the given storage space.

3. Result interpretation

Datasets might also include a considerable amount of false or otherwise incorrect information which affects accuracy.

4. Algorithm identification

To solve a machine learning problem, we will use a variety of algorithms. Running models with several algorithms and determining the best accurate method based on the findings is a laborious and time-consuming operation. All of the algorithms must run and test our data. After then, and only then, can we choose which algorithm we wish to use based on the accuracy of their results.

5. Susceptibility to errors

Going back to our Netflix example, let’s say we’re training an algorithm using data sets that aren’t large enough to be inclusive of all possible variations. As a result, we end up with biased and incorrect predictions. As a result, buyers are exposed to irrelevant recommendations. In the context of machine learning, such mistakes might start a cascade of errors that go undiscovered for a long time. When they do get detected, it takes a long time to figure out what’s causing the problem, and even longer to fix it.

Summary

As an ML engineer, it is critical to understand the benefits and drawbacks of Machine Learning. In tasks such as algorithm design, decision-making, and so on, familiarity with subject matter is of utmost importance. This article aims to steer readers in that direction.