Comparing Web Development and Machine Learning Development

AI and its offshoot, machine learning, continue to make a transformational impact across the modern business world. They rank with cloud computing and the smartphone on the list of the most critical technology innovations of the 21st century. For example, in the FinTech world, chatbots powered by AI let human customer service representatives focus on more value-added tasks, while machine learning powers the algorithms used in automated trading. This only scratches the surface of potential use cases.

Applications with various user interfaces running on different platforms increasingly leverage machine learning routines. In fact, some programs using machine learning might not even provide a user interface. Instead, the model output might appear on a report, or a spreadsheet, or simply as part of a backend process. This is increasingly the case with some mobile apps, desktop apps, and interactive websites. 

Related to web development, comparing that type of software engineering project to a machine learning project only highlights the significant differences between the two. As noted earlier, interactive web applications increasingly leverage machine learning routines for a variety of purposes. So let’s take a closer look at how machine learning benefits web development projects and vice-versa.

Using Machine Learning and Artificial Intelligence to Benefit Web Development 

AI and machine learning allow certain repetitive tasks to be automated, improving operational efficiency for a variety of use cases. In fact, many software development shops—especially those using the DevOps methodology—strive for the “Holy Grail” of continuous integration and continuous deployment. Automation depends on data science and machine learning to optimize manual processes, helping developers make application enhancements and fix bugs with higher velocity.

Software engineers increasingly rely on machine learning models to detect suspicious and potentially fraudulent network activity. Simply stated, it makes it easier to secure a web app, providing another example of how machine learning benefits the process of web development.

Web Applications Providing a Front-End to Machine Learning 

Additionally, many web applications provide a user interface around functionality using algorithms powered by machine learning models. Consider the automated stock trading functionality mentioned earlier.

A human securities trader still needs to analyze and vet the quality of these automated trades, likely using either an interactive website or desktop application. In fact, they may even work closely with the web or application development team—or separate machine learning developers—on training the machine learning models.

In those scenarios, the machine learning models and algorithms might be developed by a separate team from the application developers.

The machine learning routines might be encapsulated within an API able to be used by front-end developers building apps for one of those three platforms using programming languages like Python or Javascript. 

Web Apps Leveraging Machine Learning Powered Data Analytics 

Data remains the lifeblood of the modern business world. In fact, the massive amounts of big data generated by social media and eCommerce greatly benefit companies with the analytical chops and data scientists to find actionable information within all that data. Not surprisingly, data analysis continues to be an important reason modern businesses increasingly adopt the power of machine learning. 

Looking at online advertising offers another insight on how machine learning benefits modern web development. Internet-based advertising relies on tracking user activity to deliver a targeted ad more likely to result in a click. Having access to this data provided a veritable gold mine to technology behemoths like Facebook and Google in the early 21st century. 

However, machine learning makes it easier for smaller companies to perform real-time analysis of the data to generate more relevant ads. The benefits of this approach apply to both web development as well as generating valuable revenue streams for a freemium mobile app project delivering embedded advertising, for example. Of course, other kinds of applications beyond advertising also benefit from machine learning-powered data analytics.

The growth of the Internet of Things (IoT), especially in the business world, offers additional examples of real-time data analytics powered by machine learning. It allows web developers to craft websites with the ability to monitor factory equipment, patients in a hospital, or even tractors on a farm.

Machine learning models provide the computing horsepower to quickly glean information out of all this data. Meaningful use cases abound, improving efficiency in myriad business sectors: manufacturing, healthcare, agriculture, retail, as well as logistics, and the supply chain. 

Can Web Development Benefit a Machine Learning Project? 

We provided many examples of how machine learning development makes interactive websites more powerful with crucial business functionality. This also includes making the process of web development faster and more efficient. But what about the converse? How can a web development project actually benefit the rigorous process of crafting and training machine learning models?

The process of AI and machine learning development remains highly complex. In its current state, the programming and training of models largely require skilled software engineers and data scientists with relevant experience. Additionally, these programmers also use knowledge of linear algebra, probability, statistics, and calculus, as well as a deep understanding of data structures and database design.

However, as the use of machine learning increases in the business world, companies want to make model development easier. In this scenario, developing an interactive website used for model analysis and training democratizes this rather esoteric effort. Additionally, emerging concepts like explainable AI, deep learning, and neural networks provide insights into how a model generates its forecasts or returns a certain dataset.

Having this interaction available for business end-users on a website allows companies to take full advantage of machine learning without always depending on programmers for model creation and training. It also dovetails nicely with the No Code movement gaining traction in the business world.

In the end, web development and machine learning development remain two largely separate disciplines requiring different approaches.

Its data analytics capabilities serve to add transformational functionality for myriad use cases. The use of ML-powered automation also makes the process of web development faster and more efficient.