5 Key Challenges in Machine Learning Development

Machine learning is all about computers learning human behavior patterns. That may sound a bit creepy, but in fact, it has nothing to do with AI-powered terminators. Since its inception in the late 20th century, this technology has found practical application in many modern industries. Successful businesses tend to grow, especially in the era of the global market, when they have access to billions of potential customers. The math is simple: more customers = more data. How to handle and analyze this information flow? Well, there are different ways, but machine learning offers the most sophisticated and cost-effective solution by applying algorithms that determine the most relevant “if-then” programs automatically. As a result, data collected in the past helps the machine predict the most likely future outcomes.

Machine Learning as a Service (MLaaS)

What is Machine Learning as a Service? This concept refers to modern cloud computing services that offer machine learning tools to other businesses. Thus, developers can use the successful developments of tech giants such as Amazon, Google, and Microsoft to help other businesses put machine learning into practice. 

Logo	Company name	Platform
	Amazon	AWS Machine Learning
	Google	Google Cloud Machine Learning Engine
	Microsoft	Microsoft Azure Machine Learning Studio

So how do businesses benefit from this? Convenient and efficient pre-built AI software fully hosted by the providers makes it possible to enhance every aspect of your business through self-taught analytical tools, machine deep learning and natural language processing algorithms, automated customer interactions, machine learning cybersecurity, etc.

5 Key Machine Learning Challenges

The advantages of machine learning implications are quite obvious. But what about the challenges you might face when developing an AI/ML-powered app from scratch?

Achieving Effective Weights in ML Algorithms

To achieve the most accurate and generalized outcome over multiple iterations, the algorithm must decide which results to accept and which to reject. With each new iteration, it becomes more difficult to filter out results. To reach the convergence minima, it is important to constantly adjust the weights, which serve as a kind of “limiters,” as the model progresses. Let’s imagine a sculptor working with different tools as the work progresses. At first, he uses heavy hammers and chisels, but when it comes to sculpting fine details like facial features, he uses the finest tools available. 

In the context of machine learning models, weights are the tools that need to be tuned more and more accurately as the convergence approaches. A back-propagation algorithm evaluates the performance metrics after each iteration and decides which weights need tuning. Eventually, the minimum is reached at which the training data is narrowed down as much as possible and the model can be successfully applied to similar types of data.

Choosing the Loss Function

The choice of the loss function determines how the machine learning model will converge to useful data for further practical application. What do we call “loss” in this case? This is a metric that shows how far the neural network has deviated from its goal after another iteration. Accordingly, the lower the loss, the closer the model is to the minimum point where training data becomes the most useful for subsequent implementation and analysis. Initial processing of training data is characterized by a sharp drop in loss, but it slows down with each iteration. This is because data is getting closer and closer to convergence.  

The rate and limit of further descent along the convergence curve will largely depend on the chosen loss function. For example, we can use The Mean Absolute Error or the Root Mean Squared Error loss functions. The MAE tends to normalize “outliers” (data that deviate significantly from the mean), while RMSE focuses on large errors so it can be useful in investigating the usefulness of the training model and how consistent it is.

Implementing Learning Rate Schedules

Just like a sculptor can quickly turn a stone into something resembling a human silhouette, any machine learning model learns at a fairly high speed in the beginning. At this initial stage, the sculptor only prepares the stone for further detailing, while the model identifies major trends and generalizes potential relationships. Just like weights should be tuned as the model progresses, the learning speed should also be slowed down to concentrate power on recognizing emerging data connections. 

However, when adjusting the learning rate, keep in mind that if the rate is too low or drops too early in a training session, the system may recognize some weak local connections as a general trend. Conversely, if the rate remains high for too long, the model may come to a false conclusion and miss out on some important general relationships. There are machine learning libraries like Keras that allow you to adjust the learning rate schedules automatically based on the number of iterations or the time spent. Although configuring learning rate schedules manually based on some previous experience is also an option.

Dealing with Innate Randomness in a ML Model

Speaking of the pros and cons of machine learning algorithms, it is worth noting their stochastic nature. Although machine learning models are primarily trained to be applied later to process similar data types, it is impossible to get the same result even when working with the same dataset in practice. The thing is that data never enters the model in the same sequence, since the algorithms process and develop data randomly.

If a plane deviates at least 1 degree off course, it will be hundreds of kilometers away from its destination in a few hours. In pretty much the same way, minor changes to the initial assumptions may ultimately lead the model to a different convergence, although its course would still be adjusted to the general dataset along the way, as opposed to the airplane.  Therefore, the less consistent the dataset, the more the randomness will affect the course of the model’s development. It’s important to consider that cleaning and labeling data will help minimize the impact of innate randomness to some extent. 

Achieving Insightful Dissonance in a Dataset

The primary objective of machine learning algorithms is to detect non-obvious correlations, that is, dissonances between diverse datasets that may carry fruitful insightful relationships. However, if the data is too diverse and inconsistent, any connections may turn out to be false and non-reproducible. In this case, the entire model should be reconsidered. On the other hand, if the data is not diverse at all, and convergence is too easy to achieve, the model won’t be able to form flexible neural connections to reveal useful insights in more complicated training sessions. 

Axisbits Is Your Machine Learning Development Company

Our company is happy to offer your business vast expertise in the development and integration of ML-based solutions. We successfully deploy machine learning technologies and provide machine learning consulting for many industries worldwide, including healthcare, eCommerce, education, fintech, gaming, and entertainment. What services do we offer? The sky is the limit! You’ll be offered expert assistance with everything related to artificial intelligence and machine learning: 

• Natural language processing;
• Intelligent security;
• Predictive maintenance;
• Personalized targeting;
• Computer vision (pattern recognition);
• Big Data analytics.

Machine learning will help you solve the unsolvable and reveal new insights you would never know about. Are you ready to make your business strategy entirely data-driven and customer-oriented? Feel free to contact Axisbits! Leave an application in a few clicks and we’ll reach you out with a real offer ASAP! Our machine learning development team is always ready to take on challenging projects and deliver them on time!