Common Challenges In Machine Learning Projects And How To Overcome Them

Machine learning, a subfield of Artificial Intelligence, involves building computer systems that can learn from current and historical data without being explicitly programmed. This novel technology focuses on making machines “learn” and make decisions just like humans based on previously unknown trends and patterns observed in a dataset.

This unique ability to make data-backed decisions and identify patterns makes machine learning invaluable for advancing operations in various industries, such as healthcare, supply chain management, finances, and more. However, as businesses and organizations continuously discover and utilize its advantages, they face major machine learning challenges that make it difficult to realize its full value.

The following are some of the most common ones:

What are Machine Learning Challenges?

Machine-learning challenges are the various difficulties that businesses encounter when they try to build machine-learning solutions to power their operations. Some of them include the following:

1. Insufficient Training Data

Machine learning thrives on data. The models need to analyze a large amount of data before they can glean meaningful insights that will guide their future decisions. As such, many members of the machine learning community playfully use “garbage in, garbage out (GIGO)” as a mantra describing how machine learning models perform their functions.

The problem is that organizations, especially small and medium-sized enterprises (SMEs), don’t have access to enough data to train these machine-learning models. Although the exact quantity requirements vary across different models, there are at least tens of thousands of training examples, and most organizations simply can’t generate that much data internally.

The Solution

To remedy the challenge of insufficient training data, the following approaches can be considered:

Data Augmentation Techniques: Data augmentation is the artificial process of generating new data from existing data by creating modified variants of the existing dataset. This technique boosts the size and variety of the existing data and improves model optimization and generalizability.

Exploration of Alternative Data Sources: You also don’t have to stick with your primary data collection methods.

For instance, data domains have a wealth of data regardless of your industry or domain. To capture more information, you can also run more customer surveys and feedback forms. Furthermore, social media can also be a huge reservoir of untapped data.

The catch here is to broaden your horizons beyond your organization’s internally generated data in order to generate more data.

2. Poor Data Quality

Although machine learning models need to be trained with a sufficient amount of data, the data also has to be quality and accurate. To train models with a large amount of data, some businesses may resort to using just any data, and that has been a major machine learning challenge in recent times.

Noisy, dirty, or inadequate data can cause any machine learning algorithm to produce inaccurate predictions and, consequently, wrong decisions. Noisy data refers to a dataset containing loads of extra meaningless data, while dirty data refers to a faulty dataset containing duplicates, inaccurate data, or inconsistent data.

Poor data labeling is another challenge closely related to data quality. It means using poorly labeled data in which each input is incorrectly paired with the wrong output or, worse, with no output at all. This problem is very common with machine learning models that require specialized knowledge, such as natural language processing, medical imaging, and predictive maintenance.

The Solution

The following remedies can be applied to improve data quality:

a. Robust Data Cleaning Practices: Before feeding the machine learning model with the data gathered, it is important to carefully identify, remove, or replace any missing, irrelevant, or duplicate data from the training set.

b. Data Validation Tools and Techniques: These tools help to catch any data-related issues early on.

c. Active Learning and Semi-supervised Learning: Instead of manually labeling data, these techniques can help you learn more from unlabeled data.

3. Overfitting and Underfitting

These two terms are closely related to the data quality challenge discussed earlier. However, they represent a more specific and popular case.

Overfitting occurs when a machine learning model is made to fit too closely to or match (memorize) the training dataset. When this model fits too closely to the training set, it performs excellently with that training set but is unable to make near-accurate predictions in real-world situations. In the human context, it’s similar to overgeneralization.

On the other hand, underfitting is the opposite situation. In this case, a machine learning model is too simple to capture the relationship between the input and output variables in a dataset. Underfitting occurs when a machine learning model has not been adequately trained for a sufficient amount of time and with the right amount of parameters on training data. As a result, the model doesn’t fit right or capture the right trends on the training dataset. By extension, it is incapable of making accurate predictions in real-world scenarios.

Underfitting and overfitting are major challenges that data scientists and machine learning engineers face when building machine learning models. They are like the two extremes of the same spectrum. Therefore, having an accurate machine learning model is about finding a balance or sweet spot between these two extremes.

The Solution

a. Adjusting Model Complexity: This means reducing the model parameters to remedy overfitting and reducing them for underfitting.

b. Increasing the Data Size: Simply adding more data to the training set manually or using data augmentation techniques can help reduce overfitting

c. Adjusting Regularization: Regularization simply means adding a penalty factor to the model to ensure that it focuses on the prominent patterns present in the dataset. For overfitting, regularisation techniques incentivize the machine learning model to avoid incurring the penalty factor. On the other hand, reducing regularization helps to reduce underfitting by relaxing the penalty factor so the model can learn more from the dataset.

4. Lack of Machine Learning Expertise

If you’re not very experienced or conversant with machine learning concepts, you might have gotten a bit confused with some of the terminologies we mentioned above. More so, even machine learning engineers and professionals have to update their skills because the field is constantly evolving. New tools and techniques are being developed, and the summary of this is that building machine-learning models can be very challenging.

First, professionals need knowledge of data science, AI, and machine learning concepts. Second, they need domain-specific expertise to create and launch a model that accurately caters to a specific industry. Frankly, this requires a lot of dedication and knowledge, and there are few high-quality professionals around to pull it off.

This huge talent gap poses a major challenge to businesses and organizations that want to use machine learning technologies to tackle real-world problems. Hiring the right professionals can be time-consuming and costly.

The Solution

The challenge posed by the lack of machine learning expertise can be tackled using the following approaches:

a. Creating Cross-Functional Teams: Instead of burdening machine learning engineers with gaining industry-specific knowledge, it might be helpful to set up cross-functional teams comprising machine learning engineers and domain experts from different industries when building ML solutions.

b. Partnering With Machine Learning Development Companies: To offset the cost of hiring and maintaining in-house machine learning developers and professionals, businesses should partner with machine learning development services with expert ML engineers and developers.

In fact, reputable companies like Debut Infotech often have pre-built and tested solutions that can be easily customized to unique organizational needs. This will save you time and cost while also generating better results.

5. Ethical and Regulatory Compliance Issues 

We’ve established that data is the lifeblood of machine learning solutions. However, past events across different domains may reflect patterns of bias and discrimination that have persisted in society. The challenge is that as human society becomes more aware of these systemic issues, new policies and systems are being put in place to eradicate them.

Therefore, although the data may already contain these sentiments, we do not want to build ML solutions that replicate them. That, right there, is the challenge: the need to eliminate the bias present in the data.

Furthermore, regulatory bodies across different regions have implemented serious (and important) data collection and privacy requirements. These include the well-known GDPR and CCPA compliance guidelines for data collection, processing, privacy, and user consent. That’s not all.

When you take things further to specific industries, these data-gathering processes have even more restrictions. This poses a major challenge to data quantity, which we discussed earlier. For example, the healthcare industry imposes additional restrictions like HIPAA (Health Insurance Portability and Accountability Act) on machine learning applications.

Lastly, as people become more aware of the awesome things machine learning can do, the call for transparency and accountability has increased correspondingly. People want to understand how machine learning models arrive at their predictions and decisions.

So, developing a machine learning system that works is not enough; you must also ensure that it is compliant with data regulations, transparent, accountable, and free of bias. This means businesses must consider many things when developing ML solutions.

The Solution

These ethical, regulatory, and transparency challenges can be remedied with the following strategies:

a. Bias Detection Techniques: Before feeding a machine learning algorithm with data, it is important to run the training data through fairness-aware algorithms and metrics as well as data preprocessing measures. This will ensure that the ML models do not discriminate against individuals or groups based on their race, gender, sexual orientation, social status, or other traits.

b. Prioritizing Consent: To comply with data collection requirements, it is important to obtain explicit consent for data use and collection and maintain transparency during data usage.

c. Clear Governance Frameworks: Don’t just launch an ML model because it works. Rather, ensure your organization sets up clear governance AI frameworks to guide all development processes. This keeps the entire process mindful of transparency and accountability.

7. Cost and Resource Requirements

We briefly discussed this challenge when we discussed the lack of skilled ML engineers and the costs associated with hiring the right personnel. However, developing top-quality machine-learning solutions involves other costs.

Acquiring the huge amounts of data that machine learning models need for proper training can be highly cost-intensive. Consider the costs associated with running multiple customer surveys and feedback forms. The same applies to storing and processing the data, as the computers required to run these algorithms need high computing power.

As such, companies building machine learning solutions have to incur huge initial financial investments. Yet, that’s not all they have to bother about. After launching these models, they still need to maintain regular updates, monitor the machine learning infrastructure, and even update data as changes occur in the real world. So, it is a lot to deal with as the complexities of these machine-learning algorithms continue to expand. This high cost and resource requirements can deter many organizations from adopting ML solutions.

The Solution

You can avoid the huge cost and resource requirements that are associated with adopting ML solutions by doing any or all of the following:

a. Leveraging Open-source Data: Open-source datasets are free to use. You can start training your ML algorithms with them before gradually building your proprietary datasets.

b. Leveraging Cloud Storage and Processing Systems: Instead of dropping a fortune building your in-house storage and processing infrastructures, you can use cloud storage services for a fraction of the cost to get your solutions up and running in no time. These services are also scalable, as you can start with little storage capacity in the early stages and upgrade as your needs expand. It’s better than incurring huge costs upfront.

c. Automating Updates and Monitoring: Manually checking the ML infrastructure will take time and money. Instead, set up automated alerts for performance degradation so you can quickly address any issue before it gets out of hand.

Summary: Major Machine Learning Challenges and Their Solutions

The major machine learning challenges and their possible solutions have been summarized in the table below:

Conclusion

Despite its numerous potential benefits, machine learning faces a number of challenges that deter its adoption. However, with the right approach, you can make the most of the situation.

If you’re having a hard time gathering enough data, try exploring more data sources like open-sourced projects or increasing the amount of data you have with data augmentation. However, you need to implement robust data cleaning practices and data validation techniques to maintain the quality of that data before using them. Furthermore, ethical and regulatory issues that crop up can be remedied with bias detection techniques and clear governance frameworks.

As machine learning continues to grow, more of these challenges will continue to unfold. However, with the right help from machine learning consulting firms like Debut Infotech, you can tackle them with the right insights. That’s the way your business can get the best of this awesome piece of technological advancement.

Frequently Asked Questions (FAQs)

Q. What are the main challenges of machine learning?

The main machine learning challenges include insufficient training data, poor data quality, overfitting, underfitting, lack of machine learning expertise, cost requirements, and ethical and regulatory compliance issues.

Q. What is the hardest part of machine learning?

While there are many things to consider when building machine learning solutions, data remains one of the hardest factors determining the success of these projects. As such, many machine learning engineers and developers consider data to be one of the hardest parts of machine learning.

Q. What makes machine learning difficult?

For individuals interested in becoming machine learning experts, the need for advanced programming languages has been cited as one of the factors making machine learning difficult.

Q. What is the difference between AI and machine learning?

While artificial intelligence refers to the more general idea of developing systems that are capable of reasoning, learning, and problem-solving, Machine Learning (ML), on the other hand, is a subset of artificial intelligence that focuses on creating algorithms that let computers learn from and predict outcomes based on data without explicit programming.

Q. Is chatGPT AI or Machine Learning?

ChatGPT is a type of generative AI that allows users to give commands to receive AI-generated images, text, or videos that look human.