The use of machine learning in apps and web apps is becoming increasingly popular. With machine learning, you can analyse large amounts of data to gain insights, automate tasks, and more. But, if you’re not sure how to get started, it can be intimidating.
What are Machine Learning (ML) and Artificial Intelligence (AI)?
Machine learning is an application of artificial intelligence (AI) that allows systems to automatically learn and improve from experience without being explicitly programmed. In other words, machine learning algorithms use data sets to detect patterns and make decisions with minimal human intervention. These algorithms are used in many applications, such as image recognition, language processing, fraud detection, and more.
This form of AI is becoming increasingly popular due to its ability to create more efficient and accurate business solutions. Machine learning can improve user experience, reduce operational costs, and increase revenue. Furthermore, machine learning can be used to automate processes and provide more accurate predictions.
AI is about Training your 'Model'
To use machine learning, you need to create a trained model or use an already trained model.
But what is a model?
Think of a trained model as a statistical prediction-making engine that the computer can use to recognise patterns in data and make decisions based on those patterns.
To train a model, you need to pick a statistics technique and feed it data. This data can come from sources like a database, spreadsheet, API (Application Programming Interfaces), or even user input. The data is used to “train” the model, which means it can gradually recognise patterns and make decisions based on the data it’s been given.
You can then use the trained model to make a prediction. For example, suppose you’re building a recommendation engine. In that case, you can use the trained model to identify patterns in user behaviour and recommend products or services that are likely to interest the user.
Ways to Incorporate Machine Learning into your App Business
There are five main machine learning algorithms, each suited to solving a particular problem. Here are some of the different types of problem that ML algorithms can solve:
- Classification algorithms: Classification algorithms are used to predict which category an item belongs to. For example, you could use a classification algorithm to predict whether a customer will purchase a product.
- Regression algorithms: Regression algorithms are used to predict numeric values. For example, you could use a regression algorithm to predict the sale price of a house or the stock price of a company.
- Clustering algorithms: Clustering algorithms are used to group items into different clusters. For example, you could use a clustering algorithm to group customers into different segments based on their purchase behaviour.
- Recommendation: ML algorithms can make personalised recommendations, such as recommending a product or service to a customer based on their past behaviours.
I appreciate you probably care more about the practical, real-world ways these techniques can be used; So, here are some ways to use AI and Machine Learning to drive customer engagement, optimise business performance, or save money:
- Automated Recommendations: Leverage machine learning to create tailored, automated recommendations for customers based on their individual preferences and buying history. This can help customers find the perfect product or service more quickly, and can also increase loyalty and engagement.
- Chatbots: AI Chatbots are a great way to provide quick, personalised customer service. By leveraging AI, businesses can create chatbot systems that can understand customer inquiries, respond quickly and accurately, and even provide personalised recommendations.
- Image Recognition: Use image recognition to power powerful search functions. This can allow customers to search for products or services by uploading an image or taking a photo of something they are looking for.
- Automated Content Curation: Use machine learning to curate customer content. This can help to ensure that customers are served relevant and interesting content that is tailored to their interests.
- Optimise Targeted Messaging: Leverage AI to send targeted customer messages based on their behaviour and interests. This can be a great way to keep customers engaged and increase conversion rates.
- Automate customer service: Machine learning can automate customer service tasks, such as responding to customer inquiries (or writing responses that can then be edited by a human), providing customer support, and resolving issues. Using natural language processing, machine learning algorithms can understand customer requests and provide an automated response. This can help to reduce the amount of time spent on customer service tasks and improve customer satisfaction.
- Content and product recommendations: Machine learning can be used to personalise content and product recommendations for customers. By understanding user behaviour and preferences, machine learning algorithms can recommend products and content most likely to interest customers. This can help to increase customer engagement and boost sales.
- Fraud detection: Machine learning can detect fraudulent activity, such as fraudulent payments and logins. By analysing user behaviour and data, machine learning algorithms can identify suspicious activity and alert businesses to potential fraud. This can help to reduce losses from fraudulent activities and improve security.
- Predictive analytics: Machine learning can be used to predict future outcomes, such as customer behaviour or sales trends. By analysing past data, machine learning algorithms can identify patterns and predict future trends. This can help businesses to make decisions based on accurate data and improve their decision-making process.
- Automate repetitive processes: Machine learning can be used to automate processes, such as data entry and document processing. Using natural language processing, machine learning algorithms can understand text and automate tasks such as filling in forms and processing documents. This can help to reduce the amount of time spent on manual tasks and improve efficiency.
- Improve inventory management: Machine learning can improve inventory management, which helps businesses save money on storage and shipping costs. Machine learning algorithms can analyse customer data to identify which products should be stocked and when, as well as track inventory levels and predict when new inventory needs to be ordered.
- Optimize marketing campaigns: Machine learning can be used to optimise marketing campaigns, allowing businesses to target the right customers with the right message at the right time. This can help reduce marketing costs and increase the return on investment for marketing campaigns.
- Automate financial processes: Machine learning can automate financial processes such as reconciling accounts and creating financial reports. This can reduce costs associated with manual financial processes, as well as help businesses identify areas of potential cost savings.
- Improve Cybersecurity: Machine learning can improve cybersecurity, allowing businesses to protect their data and systems from malicious attacks. This can help reduce the costs associated with repairing damaged systems and recovering lost data.
This is by no means an exhaustive list, but should give you a feel for what is possible.
The different types of Machine Learning (Pre-Trained APIs v.s. Custom Trained Models)
There are two main ways to incorporate machine learning into your app or web app: using pre-trained APIs or building a custom-trained model.
Both have advantages and disadvantages, so it’s important to consider your specific needs before deciding which approach to take.
Using pre-trained APIs (Application Programming Interfaces) is a great option if you’re looking for a quick and easy way to add machine-learning capabilities to your app or web app. APIs provide access to pre-trained models that you can use as is or customise to suit your needs. The advantage of using APIs is that they don’t require much technical knowledge or expertise, and they can be integrated quickly and easily. However, they can often be limited in terms of the type and complexity of machine learning tasks they can handle.
If you’re looking for a more powerful and flexible solution, and have some R&D money to spare, then custom-trained models may be the way to go. Custom-trained models allow you to develop your machine-learning system from the ground up, giving you complete control over the type of machine-learning tasks you can achieve. The downside is that this approach can be more complex and time-consuming, requiring a high level of technical expertise to be successful.
Custom AI models can be broadly divided into two categories: supervised and unsupervised. Supervised AI models are used to predict outcomes based on labelled data, while unsupervised AI models are used to identify patterns in data without pre-existing labels.
Supervised AI is a type of AI where the machine is trained with labelled data and given a goal it must achieve. This type of AI typically requires a large amount of data and a lot of training time, but it can be very effective.
Unsupervised AI is a type of AI where the machine is not given any labelled data or a specific goal to achieve. This type of AI usually involves a lot of trial and error and can be very time-consuming. However, it can be incredibly useful for tasks such as anomaly detection and clustering. Both supervised and unsupervised AI can be used in your app project, and it is important to understand the differences between them before deciding which type is best suited for your project.
Pre-made machine learning APIs will have been trained via either one of these methods, but you don't need to worry about that. Instead, you can connect to the service which solves the business problem you face. Using pre-trained APIs can save you time and money since you don’t have to devote resources to designing and implementing an AI solution. Pre-made APIs can also provide more robust capabilities than a custom-built solution since they are typically designed by experts and tested rigorously.
Ultimately, the choice between using APIs or building custom-trained models will depend on the complexity of the machine learning tasks you need to solve, as well as your budget, timeline, and technical capability. Training a custom machine learning model can be prohibitively expensive for some AI problems, so if you’re looking for a quick and easy solution that leverages what already exists, then APIs may be the best option. But if you have a highly unique problem plus the resources and expertise to build a custom-trained model, then that approach can offer greater flexibility and power.
AI Can Go Deeper, Much Deeper (Enter Deep Learning)
Deep learning neural networks are a subset of Artificial Intelligence that is based on the structure of the human brain.
Deep learning neural networks use algorithms to process large amounts of data and to identify patterns and connections between them. Imagine an interconnected web of dots and lines, each with different weights and values. Amazingly, you can give this web some inputs, and it gives some outputs. After some time and training, this web of connections can learn to solve extremely varied and complex problems. This brain-like structure allows them to make predictions and decisions about new data that are based on the patterns and connections that it has learned.
Deep-learning neural networks are self-learning. This means that they can refine their algorithms as they go, allowing them to become smarter over time as they encounter more data. This means they can continually improve their performance, making them ideal for applications where accuracy and decision-making are important.
Additionally, deep learning neural networks are better able to handle unstructured data. This makes them particularly useful for tasks such as image recognition and natural language processing, where the data is not organized into neat rows and columns.
Generally, it's cheaper to use a pre-trained AI model than make your own, and if you do make your model, then the more simple the statistics modelling technique needed to solve the problem, the less time is required to solve the AI problem. This means deep learning problems require lots of training data, computer resources, and highly skilled data scientists to solve, making them the most expensive way to implement AI in your business. But this expense comes with big benefits; if you can spend the resources to solve the problem, then the cost and technical challenges you encountered become a large barrier to entry to your competition.
How to use Amazon Web Services (AWS) to Train and Host a Model in your Apps
Now we know what’s possible, let’s learn the practicalities of how to use these AI techniques in your business.
One of the most popular ways to use machine learning in a mobile or web app is to host a trained model on AWS (Amazon Web Services). With AWS, you can host a trained model and make it available to your app or web app.
Here's how you can use AWS to host a trained model for use in your mobile or web app:
Step 1: Choose the Right Machine Learning Service.
First, decide which machine learning service you want to use. AWS offers various machine learning services, such as Amazon SageMaker and Amazon Rekognition. Each service has different features and capabilities, so it's important to choose the one that best meets your needs. For example, some of these services use a pre-trained model, and some require you to train and host your own.
Step 2: Train the Model
Next, you'll need to train the model. This involves feeding the model data and then teaching it how to make predictions. Depending on the type of machine learning service you choose, there are different ways to train a model.
Step 3: Host the Model on AWS.
Once the model is trained, you'll need to host it somewhere; AWS is one popular option for model hosting. This is done using an AWS service such as Amazon Elastic Compute Cloud (EC2). You can use EC2 to host your model in the cloud, making it available to your app or web app.
Step 4: Connect the Model to Your App or Web App
Finally, you'll need to connect the model to your app or web app. This can be done by using an API (Application Programming Interface). An API allows your app or web app to communicate with the model and send data for analysis (Scorchsoft has a lot of experience making and using APIs, so please let us know if you need help with this!)
By following these steps; you can easily host a trained model on AWS and use it in your app or web app. With AWS, you can quickly and easily add powerful machine-learning capabilities to your product. Google Cloud, IBM Watson, and Microsoft Asure all offer competing Machine Learning services, so check those out too.
Cost of Using AI Web Services (Such as Amazon Web Services)
The pricing of machine learning web services largely depends on the type of machine learning service you are using and the resources you are consuming.
For example, Amazon Web Services (AWS) offers a pay-as-you-go model for many of its machine learning services, so you only pay for what you use. You don't have to pay upfront or commit to a long-term contract. You connect with the service and pay each time you use its service to generate content or make a prediction.
For other services, such as Amazon SageMaker, AWS offers a combination of on-demand and reserved instance pricing models. With on-demand, you pay for the resources you consume hourly. Reserved instances allow you to pay a lower rate for a longer period if you are willing to commit to a certain amount of usage.
The amount you will pay normally depends on how aggressively you use the AI services. If you make many requests frequently, then expect to pay more than for infrequent use. Expect costs to scale with use.
When is Machine Learning Overkill and Not the Right Solution to your Objectives?
Machine learning should only be used in some app development projects, and ML is overkill when a simple algorithm would suffice. For example, if you are creating an app to display a list of items, a simple algorithm can be written to sort the list of items in the order that you wish to display them. In this case, using machine learning would be unnecessary.
On the other hand, machine learning can be a great tool for more complex tasks where you need to automate difficult predictions. If you are creating an app with a feature that requires making decisions based on a large amount of data, machine learning could be beneficial.
I have a confession to make. I wrote this entire article using OpenAI GPT3, an AI copywriting tool.
I asked the OpenAI tool to generate a list of suggested blog sections from my proposed title, “How to use machine learning in your app or web app (the easy way)” I then selected the ones I liked or wrote my own using its suggestions as inspiration.
I then provided each chosen section title to the AI to automatically generate the content.
The AI sometimes returned correct answers; however, sometimes, the responses weren’t quite right and required removal or curation. I also peppered the copy with some of my tone and knowledge to add value.
Sometimes I found the copy generated to be repetitive or full of filler words and sentences that add little to the context of the writing. I suspect this is because it was trained on open website data, and many blog authors like to use filler words to make up word counts. I addressed this by running the AI tool several times with similar but slightly different instructions where I vary the question or add additional context.
You can also ask the AI to write in a certain tone, so if it gave too simple of an answer, I could change the tone to see if it gave a more useful approach. I then choose the best sentences or paragraphs to make my curated section. Finally, I used Grammary for Business (Another part-AI tool) to make sure that the copy was on-brand and readable.
I’d equate the process to asking a copywriter who knows nothing about a topic to write based on what they read online. They may be able to find interesting facts and information to reference. Still, unless they are an expert on the topic, they cannot critically evaluate whether what they are writing is contextually correct.
Don’t worry; I’ve not fed you a load of false rubbish. Everything in this article is correct, in my opinion; I’ve reviewed it personally (This article still took a few hours to create). I don’t think these tools are a replacement for people (yet) and are best used alongside a human to delete, verify, improve, and combine the AI suggestions.
Oh, and all of the pictures are AI-generated too, using Dall-E 2! ;-) If you are curious about what this costs, we spent approximately $5 generating images and $0.19 generating copy. We could have spent a lot less on images, but the process of finding and editing the right picture involves a lot of trial and error, which burns through credits,
If you’re looking for an easy way to incorporate API-based machine learning or existing trained models into your app or web app, look no further than Scorchsoft. Our app development company in Birmingham, UK, can help.
We can also enable your app to collect, store, and label data in a way that is compatible with modern machine-learning techniques. This data can then be used to train a model, which can host and embed within your application.
(Yes, those two paragraphs were mostly AI-generated too!)
UPDATE (11/12/2022) A Word of Caution About AI Copywriting
Wow, I thought, What an amazing exercise! I could go from nothing to a 3500-word published article in a couple of hours. Would I ever need to write or pay a copywriter ever again?
Then, I wondered if the copy would get flagged for plagiarism. After all, the AI is using the data from its trained models to make predictions.
Yes. Yes, it does.
And not just a little bit. There was "Significant plagiarism detected" by Grammarly. So a word of warning with these tools, you may be using carbon-copied text from elsewhere. And a word of caution for all website owners out there; If Grammarly knows, Google knows (if not now, then soon). Meaning AI copywriting is unlikely to be an easy Search Engine Optimisation win!
Here is the Grammarly plagiarism checker tool I used if you would like to perform plagiarism checks yourself.
UPDATE (07/06/2023) I was wrong about my previous update
Since writing the above article, I have conducted extensive research into Large Language Models and tools like GPT. I've discovered their strengths and weaknesses, and I firmly believe that this technology will transform the way we conduct business. In my last update, I expressed concern that AI language models were simply repeating text from elsewhere on the internet. While this can be true when prompted with very simple text, providing the AI with a sufficiently unique prompt usually results in a more original response. Furthermore, I have learnt about the 'impossibility result', which concerns tasks, such as detecting plagiarism, that are essentially impossible to address with any degree of accuracy. Consequently, it has become apparent that plagiarism detectors perform only slightly better than random 50/50 chance, rendering them unreliable for detecting AI-generated text on a case-by-case basis.
Regarding SEO, Google has stated that it will not penalise users for employing AI, provided that the website pages focus on delivering value to the reader and ensuring a positive user experience. This may be because they plan to release their own AI tool, Bard. Google has maintained a consistent stance on automated and AI-generated content, asserting that using AI to manipulate search results constitutes a breach of its spam policy. Nevertheless, Google has emphasised that not all automated or AI-generated content is deemed spam.
Lastly, I have authored a book on utilising AI language models, such as ChatGPT, in the workplace and business settings. Please feel free to contact us if you are interested in obtaining a copy.