Machine Learning challenges

ONPASSIVE

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5 Jul 2022
7 Min read

What are the 5 common Machine Learning challenges and how can you solve them?

According to a report by Fortune Business Insights, the global machine learning market is expected to increase from $15.50 billion in 2021 to $152.24 billion in 2028. Businesses worldwide look forward to machine learning technologies to help them solve problems and deliver insights. Even while the advantages of machine learning are becoming apparent, many companies are still having trouble using it.

Machine learning, as the name implies, entails algorithms that iteratively learn from the given data set to enable systems to learn from existing data. As a result, techniques can discover hidden insights without explicitly specifying their search criteria.

How often have you heard about artificial intelligence (AI), big data, and machine learning? Probably too frequently. You could have encountered several salespeople attempting to sell you their “new and revolutionary AI software” that would automate everything if you use a professional social networking site like LinkedIn. Machine learning has become so hot that businesses have invented unfounded beliefs about it. However, now let’s learn its importance and top 5 challenges. 

Why is machine learning important for your business?

Businesses today have the knowledge they need to act faster than ever before on data-driven choices that are better informed. It’s not the mythological, miraculous procedure that many portray it to be. Machine learning has its own unique set of difficulties. Here are five typical machine learning issues and solutions for each.

1. Recognizing the processes that need automation

Nowadays, it’s getting harder and harder to tell reality from fiction in machine learning. Before choosing which AI platform to employ, you must assess the issues you hope to resolve. The tasks carried out manually daily and have a fixed output are the simplest to automate. Before automation, complicated procedures need more examination. While machine learning may undoubtedly aid in the automation of some processes, not all automation issues require it.

2. Poor data quality

The machine learning method heavily relies on data. The lack of high-quality data is one of the significant problems that machine learning experts encounter. It may be exceedingly taxing to analyze noisy and erratic data. We don’t want our system to produce unreliable or flawed predictions. Therefore, improving the result depends on the quality of the data. As a result, we must ensure that the data pretreatment procedure, which involves eliminating outliers, filtering out missing values, and eliminating undesired characteristics, is accurately carried out.

Overfitting and Underfitting:

What is overfitting?

To give you an idea, imagine that one day you are out shopping when a dog appears out of nowhere. You give him something to eat, but instead of eating, the dog begins to bark and chase you, but you manage to stay safe. You could believe that all dogs are not worth treating properly after this specific instance.

Thus, over-generalization is something we humans tend to do frequently, and regrettably, if a machine learning model is not carefully considered, it will likewise overgeneralize. This is called overfitting in machine learning, when a model performs well on training data but struggles to generalize effectively.

When our model is very complicated, overfitting occurs.

We can perform the following things to solve this issue:

1. Make the model easier to understand by choosing one with fewer parameters.

2. Cut back on the number of qualities in the training set.

3. Limiting the model.

4. Accumulate further training data.

5. Decrease noise.

What is underfitting?

Underfitting is the reverse of overfitting, and you guessed it right. This occurs when our model is too essential to conclude the data. If you employ a linear model, for instance, on a multi-collinear set, it will undoubtedly underfit, and the predictions on the training set will inevitably be incorrect.

We may take the following steps to solve this issue: 

1. Choose a more complex model with more parameters.

2. Provide training on relevant features

3. Minimize the restrictions

3. Poor Infrastructure

The ability to process enormous volumes of data is necessary for machine learning. Legacy systems often can’t keep up with the strain and break down. It would be best to ascertain whether your system can support machine learning. You should upgrade, adding flexible storage and hardware acceleration if it can’t.

4. Implementation

When organizations decide to upgrade to machine learning, they would already have analytics engines at their disposal. It is challenging to incorporate more recent machine learning techniques into more established methods. Implementation is greatly facilitated by maintaining accurate interpretation and documentation. Implementing services like anomaly detection, predictive analysis, and ensemble modeling may be made considerably simpler by working with an implementation partner.

5. Shortage of qualified resources

Machine learning and deep analytics are still relatively young fields of study. As a result, there aren’t enough qualified workers to manage and provide analytical information for machine learning. Expertise in a particular field and an in-depth understanding of science, technology, and mathematics are frequently required for data scientists.

Paying high compensation when hiring will be necessary since these workers are often in demand and are aware of their value. Additionally, as many managed service providers have a list of qualified data scientists available at all times, you may ask them for assistance with staffing.

To sum up:

Each company is different, and each journey is unique. But in essence, fundamental problems like corporate goal alignment, people’s thinking, and more are among the machine learning concerns that businesses encounter most regularly. Budgeting following several checkpoints along the way works effectively to accommodate the organization’s affordability.

Organizations are using machine learning to make sense of their data, automate business procedures, boost productivity, and eventually boost profitability. And while businesses are eager to employ machine learning algorithms, they frequently have difficulty beginning the process.

You may seek advice from companies with the knowledge and experience in machine learning projects if you are unsure of the talent needed to build a full-fledged machine learning algorithm. 

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