8 Major Challenges Machine Learning Practitioners Face

Terms like “Machine Learning” or “Artificial Intelligence” are still not quite familiar to most of us. Many people think of “Skynet” when they first hear these words. However, let me reassure you that there are no chances of a self-aware bot attack if you are dealing with machine learning. Although we are yet to reach the “Jetsons” era, what we have achieved with AI is nothing short of exemplary. Young students are also showing more interest in AI and machine learning and are seeking my assignment help from the same. However, despite marching confidently in the digital era, there are still some challenges that need to be addressed. Let us take a look at eight such issues –

1. Data Collection

The importance of data is undeniable in the time of information technology. Whatever the case is, data plays a vital role in it. According to statistics, the major part of the key responsibility areas of a data scientist is collecting data. Beginner-level employees easily find data from the UCI ML repository or Kaggle. However, after a few years on the job, the situation changes.

Finding data becomes tougher since they have to gather data by web-scraping or through APIs, For example, Twitter. In a real-world scenario, they have to solve business problems by gathering data from the clients. So, machine learning engineers try to coordinate with domain experts for data collection.

Once the data is collected, they need to make the data structure and store it in the database. For this, the employees need to have Big Data knowledge, which proves to be a problem.

2. Less Amount of Training Data

When you are making a machine learning model, it is no different than a baby. Just like babies need food for nourishment, this model needs data to “nourish” itself. You have to teach the model everything using codes so that it can differentiate between a dog and a cat. However, the similarity ends just there.

A human child will be able to distinguish between two animals after repeating it for some time. The human brain can store and process features, colours, shapes, etc., and remember what they have been taught. However, training a model is not so easy. An AI or machine learning model don’t have a brain to process information. So, you need to feed tons of data to explain even the simplest of problems.

If you are dealing with complex problems like Speech Recognition or Image Classification, you may need to process millions of data. So, the amount of data always falls short while training the models.

3. Non-representative Training Data

The training data should ideally cover all the cases that have and will occur. But since non-representative data is used, the training models don’t get trained to make accurate predictions. Systems developed to make accurate predictions in generalised cases are considered good learning models from a business problem-solving viewpoint. That kind of data helps the machine learning models perform well even if they have never seen the data.

But if there are low quantity of training samples, we start to get sampling noises. These are the unrepresentative data, which also give rise to the problem of sampling bias.

Sampling bias eventually leads to wrong predictions, which can be demonstrated with a famous example – In the US presidential election of 1936, Literary Digest conducted a huge survey on more than 2 million people. While their votes showed that Landon would win by getting 57% votes, eventually, Roosevelt was victorious with 62% votes.

4. Poor Data Quality

In the real world, data scientists don’t start training the model directly. They start by analysing the same. However, the collected data is not always ready to be trained. Most of the time, some samples miss values or have outliers or other abnormalities.

In such cases, we fill the missing information using mean or median and remove the outliers or the attributes that are missing values before proceeding to train those. The quality of data is extremely important; otherwise, it will lead to inaccurate predictions. So, getting poor-quality of data continues to be a persistent issue.

5. Unwanted Features

The training data can contain a lot of irrelevant features. If the system gets overloaded by too many unwanted features, the machine learning system will never give the desired results. One of the most important aspects needed for a machine to run is loading it with the right features. Feature selection can have a large impact on a project. For example, if you are working on a project that can predict the hours one needs to work out based on some information, having the right features can help you get the desired result.

Let’s say the machine got the following data – gender, age, height, weight, and location. If the machine has the right features, it will immediately detect location to be an unwanted parameter for calculating the accurate hours.

Using the right features, two features can combine to perform as one useful feature, which is called feature extraction. For example, in this case, the model can combine height and weight to calculate the BMI.

6. Overfitting the Training Data

Say you have visited a new city and are looking at the menu in a restaurant. You found that the cost of the food is too high than usual. In such a situation, you may be tempted to say, “The restaurants in this city are very costly.” This is a classic case of overgeneralising. Similarly, we often categorise different frameworks in a single category, which leads to overfitting. In such situations, even if the model works smoothly, it won’t be able to make correct predictions as it is not generalised.

This can be reduced by gathering more training data, fixing data errors, reducing the number of instances, choosing models with fewer features, and removing outliers.

7. Underfitting the Training Data

This is the opposite of overfitting, and it happens when the model becomes too simple to comprehend the data structure. This can be compared with an oversized guy trying to fit into undersized pants. It happens when the data scientists fail to gather enough information to create a data model. But they anyway try to develop a linear model with non-linear information.

This can be, however, reduced by removing data noise, increasing parameters, loading with better features, and selecting a powerful tool.

8. Offline Learning and Deploying the Model

There are a few steps that machine learning engineers follow while creating an application. They are –

• Data collection
• Data clearing
• Feature engineering
• Analysing patterns
• Model training
• Model optimisation
• Deployment

The last stage is where most machine learning practitioners get stuck. They got all the skills to create and optimise a tool, but deployment proves to be challenging because of dependency issues and lack of practice.

Summing Up:

So, these are the eight challenges that machine learning practitioners often face. So, machine learning practitioners should work on these challenges and keep practising for successful deployment of the AI models.

Author Bio:

Brian Coogler is a professor of computer science and offers computer science assignment help to university students.