Machine Learning in Application Development

Machine learning (ML) has become a crucial component of application development, revolutionising how we interact with technology.

ML has an extensive history, with its inception dated back to the 1950s, and has been incorporated in popular internet applications for decades.

After taking a hit in 2022 the market for ML has recovered, largely thanks to the success of OpenAI’s ChatGPT 3.0 & 4.0 in 2023.

In 2024 the global ML market value is projected to grow further and reach $204.3 billion, with an estimated annual growth rate of 17.15%.

This would result in a market value of $528.1 billion by 2030, and it could potentially rise even higher.

As the demand for intelligent and personalised experiences continues to rise, incorporating machine learning algorithms into applications has become paramount.

From predictive analytics to recommendation systems, ML enables applications to adapt and evolve based on user behaviour and preferences.

In this article, we’ll dive into some practical approaches for leveraging machine learning in application development.

Understanding Machine Learning in Application Development

In one of our previous articles, we went over the fundamentals of ML, so we’ll be building on this to discuss how ML can be used in application development.

With their ability to learn from patterns and make predictions, ML algorithms enhance applications in various ways.

There are three main types of machine learning algorithms:

Supervised Learning: Involves training a model on labelled data to make predictions or classify new data points. This is often used in email spam detection, as the algorithm learns patterns in the email content and metadata to distinguish between spam and legitimate emails.
Unsupervised Learning: Deals with unlabelled data, clustering similar data points or reducing dimensionality. This can be used for customer segmentation, clustering customers based on their purchasing behaviour, demographics, or other features. This information can be valuable for targeted marketing campaigns, product recommendations, and tailoring services.
Reinforcement Learning: Focuses on learning optimal actions through trial and error, often used in decision-making tasks. This has been successfully applied to train agents to play complex games, such as chess or video games. AlphaGo, a reinforcement learning-based system developed by DeepMind, achieved superhuman performance in the game of Go by learning from millions of game simulations and self-play. It even created new moves that had never been seen by the best Go players before.

By incorporating machine learning into application development, developers can create smarter, more adaptive software that delivers personalised experiences and drives innovation in various industries.

Machine Learning in Action

Let’s take a look at some specific examples of how ML algorithms are currently being used in a variety of different popular applications:

Netflix: Every time you watch a movie on the platform, ML algorithms are working away in the background to figure out how much you enjoyed it. Data such as your watchtime is used to predict other content you may like, and prioritise it in your recommendations feed.
Snapchat: ML, in the form of computer vision, is used to identify users’ faces, whilst augmented reality creates a 3D mesh to apply different filters and effects to enhance user photos and videos in real time.
ChatGPT: Large language models use ML to understand and generate human-like text responses. These models are trained on vast amounts of text data, learning the relationships between words, phrases, and concepts. Through a process called fine-tuning, the model adapts to specific tasks or contexts, such as answering questions or engaging in conversation.

Machine Learning by Industry

Looking at a broader scope, every industry benefits from ML algorithms in one way or another, shaping their operations and outcomes.

Let’s take a look at some of these industries and how they use ML to drive innovation and efficiency

Transportation: The most impactful implementation of ML in the transportation industry has been self-driving cars, with brands such as Elon Musk’s Tesla leading the way. AI and ML algorithms are used to predict changes on the road and monitor traffic, which has the potential to significantly reduce road accidents in the future.
Healthcare: Disease discovery and treatment are greatly benefiting from ML algorithms, as they can process large amounts of data from blood tests and MRI scans. As these algorithms keep improving, they have the potential to identify patterns in genetics that lead to chronic disease, so that healthcare workers can take action much earlier.
Finance: Fraud has always been a huge problem in the financial sector, however AI and ML are proving to be effective tools for fraud detection and prediction. They do this by analysing large amounts of data to predict future data collection, management, investment, and transactions.
Cybersecurity: ML algorithms can analyse data from incident reports, alerts, and suspicious activity to identify potential system threats. This not only improves security analysis, but can even advise a response to threats and attacks. To learn more about some past cybersecurity breaches, check out our previous article here.

Building a Machine Learning Application

Now that we know some of the use cases for ML in application development, let’s go through a step-by-step framework for implementation:

Step 1: Frame the Problem

It is important to determine the problem that we are trying to solve with ML in our application.

Here are some important questions to ask ourselves:

What is your ML algorithm trying to predict?
What are the target features? (The characteristics of the data that you want to understand more clearly)
What is the status of your target feature, and how will you measure it?
How will you acquire the input data? Will you create the dataset, or use an existing one?
Which type of ML will be best for this data? Supervised, unsupervised, reinforcement learning?
Within your ML type, which approach will be best? Binary classification, clustering, or regression?

Step 2: Collect, Clean & Prepare the Data

Whether we’re creating the data from scratch, or using an existing dataset, it is essential to remove any irrelevant data inputs.

The accuracy of your ML algorithms future predictions will be directly affected by the quality of the input data.

It’s also important to understand that ML can only analyse numbers, therefore data such as text or images must be converted into a numerical format.

There are several ways that this conversion can be accomplished, here are some examples:

Bag of Words (BoW): For text data, this method represents each document as a vector where each element corresponds to the frequency of a word. Example: “The cat sat on the mat” could be represented as [1, 1, 1, 1, 1, 1, 0, …] where each index corresponds to a word in the vocabulary.
TF-IDF (Term Frequency-Inverse Document Frequency): Similar to BoW, but also considers the importance of words by penalising common words that appear in many documents. For example you might give higher weights to less common words.
Word Embeddings (Word2Vec, GloVe): These methods represent words as dense vectors in a high-dimensional space where the distance between vectors corresponds to the similarity between words. Example: “King – Man + Woman = Queen”, where word vectors can capture semantic relationships.
One-Hot Encoding: For categorical variables, each category is represented as a binary vector where only one element is 1 and the rest are 0s. Example: [1, 0, 0] for ‘red’.
Image Pixel Values: For images, each pixel value is treated as a feature. Colour images typically have three channels (red, green, blue), so each pixel is represented by three values.
Convolutional Neural Networks (CNNs): CNNs learn hierarchical representations of images by applying convolutional filters to capture patterns and features at different scales. Example: CNNs can learn to recognize edges, textures, and objects in images.

Step 3: Feature Engineering

Sometimes row data doesn’t contain any specific characteristics that are required for the ML algorithm to operate correctly.

This is where feature engineering becomes important.

This technique creates additional features by combining two or more data features into an arithmetic operation.

This makes them more relevant, and in a sensible format that won’t lead to reducing the accuracy of the model.

Remember that data cleaning, preparation, and feature engineering are usually the most time consuming steps in the ML development process.

They’re also the most important, as they will impact training accuracy, so take your time here.

Step 4: Training the Model

Before the ML model training starts, the data must be split into three parts: training, validation, and testing.

The ratio of this split should be 60-80% training data, 10-20% validation data, and 10-20% test data.

For smaller datasets, it’s better to use less training data so that you have more data for validation and testing.

The training set is reserved to fit the model, so that it can learn and directly improve its parameters.

The validation set is used to fine-tune the model during training, helping to assess performance and adjust accordingly.

This provides us with insights into the models ability to generalise to unseen data.

The testing set is used to evaluate the final performance of the model, serving as an unbiased measure of the models accuracy and generalisation capabilities in real-world scenarios.

There are many ways that this model training approach can be modified, including controlling learning rate, regularisation, number of passes, and more.

Step 5: Evaluate and Implement

Overfitting occurs when the model is too specialised to the training data.

This results in the model becoming unable to generalise and make accurate predictions using new data.

Underfitting is when the model is too simple.

This could be the result of it requiring more time with the training data, more input features, or less regularisation.

Once optimised so that it isn’t either overfitting or underfitting, your model is ready to be deployed in your application.

When integrating your ML model, remember that it is simply a prediction machine, based on accurate data.

Tech Stack

As you can see, building a ML model from scratch can become a very lengthy process.

Fortunately, there are many different frameworks, libraries, and APIs that enable us to build ML applications much faster.

Let’s go through some of these frameworks that are most commonly used, and their applications:

TensorFlow: A framework from Google that is suitable for building deep learning models. These use neural networks, which you can learn more about by clicking here, to enable progressive learning and improvements.
PyTorch: A direct competitor of TensorFlow that is also part of a bigger Torch deep learning framework. This can also be used to build deep neural networks and perform complex computations.
Apache Mahout: A framework with built-in algorithms for distributed linear algebra. It focuses on tasks such as collaborative data grouping, classification, and filtering. This is ideal for any statisticians and data scientists out there.

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Conclusion

We’ve explored the pivotal role of machine learning in application development, highlighting its transformative impact across industries.

By implementing the practical approaches and leveraging the technologies we’ve recommended in this article, developers can craft intelligent, adaptive applications that enhance user experiences and drive innovation.

As you embark on your machine learning journey, don’t hesitate to experiment and explore new techniques.

Remember, the possibilities are endless.

For further reading and deeper insights, delve into the resources provided below.