If you have a great idea, and it involves creating something innovative, aiming at solving standard problems using unconventional methods and tools, it’s probably worth bringing it to life gradually. This way, you will move from priority features to secondary ones, relying on real users’ opinions—both positive and negative ones. Actually, here, we described the essence of the MVP development process. Below, we will explain its definition and check its benefits and specifics of implementation.
Understanding MVP in Software Development
If you are going to build an MVP, you should first understand exactly what it is.
In a nutshell, a minimum viable product (MVP) is a product that has basic functionality to satisfy some needs of the target audience. Its main goal is to attract first users, determine their impression of interacting with it, and think through a further strategy for its development.
Key advantages of an MVP approach
When you decide to build MVP, you get the following benefits:
Ability to confirm or refute hypotheses about the product
Attracting investors through accelerated confirmation of the idea’s viability
Help in analyzing the target audience
Reduction of financial risks
Development costs reduction thanks to proper prioritization of the product’s features
Faster engagement of the target audience, long before the launch of a finalized product version
Early bug fixing
Reduced time to market
Thus, by resorting to MVP building, you can start to earn faster. At the same time, the development of a minimum marketable product is especially relevant for startups as it allows you to find out the real needs and pain points of your target audience in practice.
Developing Your MVP: Focus on Core Functionality
Now, let's find out what the MVP process looks like.
Identify your target audience
No matter how universal your solution is, its audience still has something in common, be it goals, pain points, demographics, or something else. Therefore, before you hone your product concept, you will have to define who it will be intended for—that is, conduct user research. To do this, you need to draw up a detailed portrait of the client—gender, age, place of work, hobbies, income level, desires, needs, problems, etc.
Come up with core functionalities
Surely, you have already seen a picture on the Internet that reflects the essence of MVP—a skateboard gradually turning into a car. Formally, this means that you need to understand which functions will be fundamental, that is, without which no user will be able to implement the task assigned to your product. These will be your MVP requirements. Then, as you receive reviews from real users, you can add features of less priority that make your solution more usable than the previous version.
Prioritize correctly
Before you start to develop MVP, think about which stages of your project are the most resource-intensive—they will need to be implemented first. As resources become available, you can assign lower-priority tasks to individual members of your team. Actually, this prioritization will determine your product roadmap. Thanks to this approach, you will eliminate downtime in your work processes and will be able to bring the MVP software launch date closer.
Define tools and resources
Finally, you should determine what tools you will use in development and what resources will be needed for this. We are talking about programming languages, wireframing and software development tools, platforms for project management, business messengers, etc.
Iterating Based on User Feedback
Any MVP strategy consists of iterations—that is, constant repetition of the development cycle based on user feedback received after testing its previous version. Let's look at the nuances of implementing these iterations.
Data collection methods
Depending on the resources available, you can collect data from real users by applying different methods. This could be live interviews, questionnaires, A/B product testing, supervised and unsupervised testing, and so on.
Feedback analysis and prioritization
Once the data has been collected, you will need to identify which reviews should be processed first and which ones can be postponed. In fact, everything is simple: the highest priority belongs to those that indicate that the user cannot perform the target action in your solution at all. Second in priority are reviews related to the complexity of the functionality—that is, those in which users claim that they did not immediately figure out what to click, or that they had to spend too much time to perform a simple operation. Everything else that does not affect the accessibility and simplicity of functionality can be optimized later, in subsequent updates.
The importance of iteration
Since building MVP for startups involves creating a primitive version of a full-scale solution, most likely, you will have to deal with a lot of negative feedback. Some will not like the typography, some will find the navigation inconvenient, and some will not even understand what actions need to be performed to achieve the goal. This is okay. At the same time, those who leave you reviews now are likely to use your solution after its optimization, too—therefore, in order not to lose their loyalty, you should constantly improve it. This will show your audience that you appreciate their opinion and that they also contribute to your project.
Launching Your MVP: Getting It Out There
Each iteration that brings your MVP closer to its finalized version must fulfill a specific business task—this could be confirming or refuting hypotheses, testing functionality, attracting users, choosing the best version from several proposed ones, identifying competitive advantage, etc.
This means that with each new iteration, you may have to take different approaches to each stage of the development cycle, from MVP planning, as well as choosing development tools and startup methodology, to testing and determining methods for collecting user feedback. Ultimately, this flexibility will open up further development prospects for your minimum viable prototype and reduce the risks of its possible failure.
Conclusion
Generally speaking, the benefits of creating an MVP from a business point of view cannot be overestimated. This approach helps reduce the risks of failure, attract investors, ensure a more moderate expenditure of the project budget, evaluate its competitiveness, and much more. In addition, it allows you to quickly enter the market because instead of the time-consuming development, you can spend less time creating its primitive version and, thus, more quickly test its viability and prospects in practice.
If you are just considering the possibility of implementing your business idea in MVP format, feel free to contact us. We will take on all stages of its implementation, from concept approval to usability testing and launch, and will repeat iterations until it transforms into a full-fledged product.
With more than 2.7 billion smartphone users across the world in 2020, it’s no surprise that the mobile app industry is expended with no signs of stopping. Nowadays, smartphones have become a useful and convenient instrument that offers multiple benefits such as communication through emails and social networking, web surfing, entertainment, GPS and various other productivity apps.
In fact, studies show that the average American checks their phone every 12 minutes. 10% of people check their phones every four minutes.
We use our phones every day at work, at home, on the street, in bed, while we are eating, in our cars. You might be reading this article from a mobile device right now.
What are people doing on their phones? Well, 90% of mobile time is spent on apps.
According to these facts, many companies considering mobile apps are facing a common challenge—which app development approach to choose? Should we build a native, mobile web, or hybrid app? More importantly, which is the most appropriate app type for you and your business?
Let’s find out what are native, mobile web, and hybrid apps, analyze the differences between them, as well as the advantages and disadvantages of each. After reading, you will be able to understand which approach will be the right one for your business idea.
Web Apps
A web app is a software program that uses web technologies like HTML5, CSS, JavaScript. It exists entirely within browsers such as Safari and Chrome and is essentially a mobile website. It is therefore not possible to download them from an app store.
Also, the web app is not connected with the device’s operating system and does not use its storage. Unlike an iOS or Android app, there is no software development kit (SDK) for a developer to work with. There are templates and frameworks for developing web apps like Angular, React, and Vue.js that you can use to get a quick start.
So, here are some pros of web application
Developing a web app can be simple and quick. So, web apps are a cost-effective way to put your product in the hands of a lot of users.
Web apps can be built for all platforms as long as they can run in an appropriate web browser.
The user doesn’t have to manage updates manually. The newest version always loads when a user opens a web app.
And some cons:
Searching for an app might be difficult because the user doesn’t find it in any app store.
A browser is required to run a web app. It means that users have to take more steps to use a web app, whether that’s searching for the page or typing in a URL.
Web apps can be slower and lack a lot of benefits. They only work online and they don’t have access to as many phone features like push notifications, contacts, etc.
However, as browsers and web apps become more advanced, PWAs (progressive web apps) offer a wider functionality like:
Sending push messages
Using some touch screen elements
Using some of the device’s hardware
So, If your startup is on a budget and doesn’t require complex functionalities or access to operating system features, then building a web app can be the most suitable solution.
Native Apps
A native app is a software application that is built for specific platforms and is written in languages that the platform accepts. For example, Swift and Objective-C for native iOS apps, Java or Kotlin for native Android apps and C# for the most part for Windows Phone apps. Native apps are also built using the specific Integrated Development Environment (IDE) for the selected operating systems.
Advantages of the native application:
Native apps offer the fastest, most reliable and most responsive experience to users.
Good integration with the hardware of the device (e.g. camera, GPS, phone, touch screen)
It comes with the push notification functionality.
Great design and user experience possibilities. For example, the implementation of sophisticated UI/UX design and animation.
These apps can be downloaded from app stores and installed on mobile devices, where they can be placed as a designated icon on the home screen.
Native apps are also more suitable for working offline.
Disadvantages:
Need more than one codebase. iOS apps will not run on Android and the opposite, so you will have to work with different codebases for every platform you choose to build in.
Native apps are usually more expensive in developing, especially for companies that need apps on multiple OS platforms. For both platforms, it will require two separate developers or teams.
A lot of time is spent on development. Also, it takes time to update the app for different platforms.
Require constant updates to keep with the current OS versions.
Expensive to keep your native app updated
Both Apple App Store and Google Play get a 30% commission from every in-app payment.
To sum up, a bigger budget is required if you want to build your app for multiple platforms (i.e. iPhones and Android) and to keep your native app updated. Native apps are not the best option for simple applications.
By offering a great user experience, better performance and accessibility, native apps are able to offer users a more personalized product.
Hybrid apps
Hybrid apps combine the power of native and web apps to reap the benefits of both. Actually they are made, so they look and feel like native applications, but like web apps, are built with JavaScript, HTML, and CSS. Also, there is a shell that is downloadable and loads the code using a WebView.
Advantages of the hybrid apps:
Hybrid apps don’t need a web browser like web apps.
With a hybrid app, you only have one codebase to manage. So, instead of building two apps like with native apps, you’re building one app, and it works on both platforms.
Hybrid app developers are often less expensive than native application developers. You will probably require half the number of developers two native apps would have required. Or, with the same number of developers, a hybrid app could be published in half the time.
Hybrid is a great option for developers that create visually intense applications, for instance, games
Hybrid apps let you retain the same ability to access device features as with native apps.
Easy to work with, maintain and upgrade
Because of their native infrastructure, hybrid apps can work offline.
Hybrid apps are easier to scale to another platform. Once you’ve built for one platform, you can launch on another, like Windows Mobile.
The downside:
Getting your hybrid app to run appropriately on each platform generally takes substantial work. Sometimes, the total cost might become comparable to that of fully native apps. It all depends on how close you want to get to the “native user experience” or how simple your app is.
Hybrid apps can be slower than native apps.
Need to customize to a large number of exciting browsers
Inability to implement complex features (e.g. in-app analytics, charts, diagrams)
These hybrid systems make bug fixing more difficult.
Hybrid apps load in a WebView, so performance can be worse than native applications.
Say you have an idea for an app, and you don’t know if people will like it or not. If you understand that a web app doesn’t really allow you to test this app, the simple version of the hybrid app will be a good solution for you.
You give to people to download and use the app on their device, although you are short on resources. Usually, a startup project is decided to create a hybrid app that in the startup world, this is called an MVP, or minimum viable product.
Conclusion
There are many different directions in which you can take your app, all of which have their pros and cons. The choice between web, native, and hybrid development is dependent on a number of factors, including business needs, app requirements, developer skill, budget and timelines. What’s important is to spend enough time thinking and analyzing before you start building because the apps are expensive enough. So, you may only have one go at getting it right. Find out the information as much as you can about the different kinds of apps and the development stages.
If you regularly think about how to optimize your business operations by speeding them up, increasing their accuracy, reducing related expenses, and eliminating risks associated with the human factor, perhaps the introduction of artificial intelligence will be the best option.
Specifically, according to the CompTIA IT Industry Outlook 2024 report, today, about 22% of companies are actively seeking to integrate AI into most of their technological solutions and processes, and 45% of companies are considering its implementation. If you are still in the second category, it's time to figure out which programming language will be able to form the basis of your new highly intelligent software tools.
The term “Artificial Intelligence” describes software algorithms and systems that are aimed at solving problems typical to the human mind—such as learning, as well as pattern generation and recognition. Such algorithms are based on colossal amounts of disparate data collected from various sources. Thus, these algorithms form self-learning software solutions capable of analyzing this data and extracting valuable insights from it.
Top 10 Programming Languages for Artificial Intelligence in 2025
It is worth noting that AI-powered solutions may not be based on any software language—this can be explained by the high resource consumption of the AI algorithms that form them, as well as the lack of high-level functions. At the same time, there are ten languages that are most often used in AI programming. Which one of them? best programming language for AI?
Python
At the top of our list of programming languages for artificial intelligence is Python. This programming language appeared long before the popularization of AI development. However, thanks to its low entry threshold, open-source code and extensive compatibility, its community quickly grew, and today, Python is considered one of the three most relevant languages worldwide. Beginners can easily find support and useful resources on platforms such as GitHub and Stack Overflow.
As for the tools for artificial intelligence programming, there are a lot of Python-compatible frameworks, libraries, and APIs, aimed at simplifying and speeding up this process. Among them are TensorFlow, PyTorch, Keras, SciPy, NumPy, Scikit-learn and Matplotlib and many others. Using them, developers can create high-performance, reliable, and scalable solutions that can solve even the most complex research problems. This significantly saves time since, instead of creating everything from scratch, you can simply import ready-made tools.
One of the factors that makes Python one of the most popular AI programming languages is its simple and intuitive syntax, which ensures ease of learning.
Python is also known for its versatility, making it a top programming language for artificial intelligence among data analysis scientists and software developers. For example, it is actively used to create neural networks and machine learning algorithms with frameworks like TensorFlow and PyTorch. Additionally, Python is widely applied in data analysis, helping to identify patterns, trends, and correlations.
Best suited for: AI development, data analysis, and machine learning.
Tip: If your project involves resource-intensive computations and requires a detailed analysis of requirements, it is worth considering other AI programming languages.
R
R, being a statistical programming language, is great for data analysis and visualization. Even though developing artificial intelligence and machine learning solutions is not the primary use case for R, it is still adept at handling very large numbers. So, where popular Python may fall short, particularly in Data Science and Data Analysis solutions, R may be a better alternative.
Additional benefits of this developer tool include support for functional programming, vector computing, and OOP, as well as a number of packages specifically created for AI and ML development (for example, gmodels, RODBC, and OneR).
It includes numerous visualization libraries such as Ggplot2, Lattice, and Shiny, which allow for the quick creation of graphs, charts, and other visual representations of data. Thanks to built-in functions like GLM and LM, specialists can perform statistical modeling.
The Caret library enables building, training, and testing machine learning models, as well as optimizing their performance through hyperparameter tuning.
R also provides tools for performance evaluation, including resampling methods and cross-validation, which help developers improve their AI models. Additionally, R supports integration with other programming languages, such as Java and Python, creating a powerful ecosystem for developing various applications.
Best suited for: Statistical analysis, data processing, and visualization in AI projects.
Tip: R is the best programming language for AI in the early stages of research and experimentation. However, its use for deploying machine learning models in real-world products may be limited, as it lacks built-in compilers and efficient memory management. Moreover, it may be challenging to learn for those who do not specialize in data science.
Java
This portable and scalable programming language is actively used in the development of highly intelligent corporate applications, especially for mobile platforms. Thanks to its platform independence, Java programs can run on any operating system where the Java Virtual Machine (JVM) is installed. In addition to compatibility with popular tools for ML programming and AI programming, such as the Deep Java Library for deep learning, OpenNLP for natural language processing, and Neuroph for building neural networks, Java is easy to write and debug program code.
It is worth noting that this language is object-oriented and scalable. It also simplifies data visualization and supports Swing and SWT (Standard Widget Toolkit), which allow developers to create attractive and complex graphics and interfaces.
Java offers a wide range of built-in libraries and modules, such as JUnit and Apache Commons, which accelerate development and help create high-performance applications. For example, Java is actively used in simulations, robotics, and embedded artificial intelligence systems. Additionally, the RTSJ extension enables the development of real-time systems, particularly for robotic solutions.
Support for multithreading makes Java suitable for resource-intensive tasks that require parallel execution. The language scales well, allowing for the creation of both simple and complex applications capable of adapting to growing demands. A large user community provides access to numerous learning materials and valuable resources.
Optimal choice for: Developing scalable, platform-independent AI applications and embedded systems.
Tip: As an artificial intelligence programming language, Java is an excellent choice for large-scale business AI systems where algorithms need to be transformed into stable and reliable software. It ensures high performance, reliability, and compatibility with various devices, making it an efficient choice for building high-performance solutions.
Weakness: Verbose syntax, not specifically designed for AI.
C++
C++ is considered an extremely powerful language for AI programming and can greatly benefit developers when creating games and embedded systems. Like Python, C++ is a mature language, which does not detract from its advantages, such as flexibility and high performance. C++ has several libraries for machine learning and neural networks that help complex algorithms run faster (including MapReduce, mlpack, and MongoDB). In general, many software engineers prefer this language for building projects that require high speed, as it interacts with training modules in a production environment.
Actually, that’s why this low-level language used for AI is primarily a tool for creating complex banking software, cloud systems, and intelligent speech recognition solutions.
C++ plays a key role in developing complex software systems with a high level of abstraction. Its popularity in the field of artificial intelligence is due to its flexibility, performance, and ability to efficiently interact with hardware components.
Although machine learning libraries such as TensorFlow and PyTorch are primarily designed for Python, C++ allows working with them through APIs or bindings. This makes it possible to integrate powerful tools into developed applications without the need to build all algorithms from scratch. Thanks to support for multithreading and parallel computing, C++ is suitable for creating high-performance AI solutions and stands out among other machine learning programming languages.
Developers can manually manage memory allocation, for example, by allocating more resources for computationally intensive tasks, which reduces latency and improves performance. Additionally, using OpenCL and CUDA enables hardware acceleration, enhancing algorithm efficiency by leveraging the computing power of graphics processors.
Best suited for: Developing high-performance AI applications with hardware resource control and memory management.
Weakness: A complex language designed for system programming.
JavaScript
JavaScript is a scripting language used to add interactivity to web pages. Even though it is not as popular as the AI programming languages described above, it can be extremely helpful in implementing solutions for Data Science, one of the most promising areas for using JS.
In addition, JavaScript is the most popular programming language in the world, so you won’t have any problems finding the right AI development tools and frameworks that are compatible with it. The popularity of this coding language for AI can be explained by several factors: reliability, stability, abundance of high-level functions, libraries, and tools, as well as fairly low requirements for other technical skills of developers (it is likely that you will not have any difficulty finding dozens of AI tools that support JS, which will cover the whole scope of work related to your AI project).
Among AI programming languages, JavaScript is particularly useful for developing real-time AI applications, including virtual assistants, chatbots, and recommendation systems. By using APIs, developers can easily integrate artificial intelligence into web applications, expanding their functionality.
Since code execution occurs on the client side, this reduces server load and increases speed, which is especially important for applications requiring instant interaction.
With the emergence of JavaScript frameworks such as TensorFlow.js, Brain.js, and ConvNetJS, it has become possible to directly implement machine learning in web development. These tools allow training and deploying machine learning models directly in the browser, expanding AI capabilities in the web environment.
Best suited for: Developing interactive AI applications in the browser and integrating artificial intelligence into web projects.
Weakness: Not well-suited for mathematical computations and machine learning.
Scala
This machine learning language is concise and expressive, and is often used in big data solutions. Scala is a multi-paradigm language specifically designed to express common programming concepts in a simple, convenient, and type-safe manner.
Scala is commonly used in the development of machine learning algorithms and pattern detection in large data sets, and all this—thanks to the built-in Smile data processing library, extensive compatibility with AI frameworks and libraries (in particular, BigDL, Breeze, DeepLearning.scala, and so on), as well as its object-oriented and scalable nature.
It allows writing clear and efficient code, facilitating the creation of high-performance applications.
One of Scala’s key advantages is its default support for immutability. This means that once declared, objects do not change their state, making them easier to track and manage throughout the entire development lifecycle. Similar to C++, Scala supports multithreading and parallel computing, enabling the creation of AI applications capable of performing multiple tasks simultaneously.
Thanks to its compatibility with Java, developers can leverage tools and libraries built for Java, including Apache Hadoop and Spark, which are essential for processing large volumes of data. This helps analyze information, identify hidden patterns, and make informed decisions.
Scala also features a powerful pattern matching system, making it convenient for natural language processing (NLP). This is particularly important in areas where AI needs to recognize and analyze user input to generate appropriate responses. Scala’s object-oriented approach supports the development of specialized tools, such as for prototyping and research.
Best suited for: Developing scalable AI applications with big data support and real-time information processing.
However, Scala is not the best choice for projects requiring rapid prototyping, such as NLP or machine learning model development in tight timeframes. This is due to a limited selection of AI-focused libraries and frameworks, as well as a longer compilation process, which complicates quick testing and model adjustments.
Julia
Julia is the best programming language for AI powered scientific solutions and technical projects that require statistical data processing. This is a relatively new language with dynamic typing, parallel programming, and JIT compilation that has proven itself well due to its high speed (Julia-based projects are rarely inferior in performance to their C++-based counterparts), flexibility, and a mature ecosystem.
Many of the tools built into this language use polymorphic dispatch, which makes switching between them much faster than in other languages. Julia has gained popularity among AI programming languages for AI developers due to its high performance and rapid prototyping capabilities. It ensures fast data processing, which is critical in the field of artificial intelligence due to the vast amounts of data that need to be analyzed. Moreover, its syntax is clear and similar to Python, making the learning process easier.
Julia supports interaction with other programming languages, such as R, C, and Python. This allows developers to use powerful libraries, including TensorFlow and PyTorch, integrating them into their projects without the need to rewrite code.
With built-in support for parallel computing and distributed computational processes, Julia is capable of effectively handling large datasets and significant computational loads without a noticeable drop in performance.
As an open-source language, Julia provides access to libraries developed by the community, saving time and effort during development. Notable tools include Plots.jl and Gadfly.jl for data visualization, MLJ.jl used for machine and deep learning, as well as Scikitlearn.jl, which is an interface to the Python library scikit-learn.
Best suited for: High-performance AI applications that require fast data processing and easy integration with other languages.
Weakness: Newer language, smaller ecosystem.
Haskell
Haskell is based on the functional programming paradigm and uses key concepts such as pattern matching, higher-order functions, and immutability. This makes it suitable for handling complex data structures in the field of artificial intelligence.
The language has a strong mathematical foundation, which contributes to the development of complex algorithms and even deep learning models. Thanks to lazy evaluation, expressions are only evaluated when needed, allowing for more efficient resource usage.
Haskell's strong type system and type safety control help avoid errors related to incompatible values in variables, improving the reliability and stability of AI applications developed in this language.
Best suited for: Creating AI systems with complex algorithms and a focus on functional programming.
Weakness: Abstract, rarely used in the field of artificial intelligence.
Lisp
Lisp is a high-level functional programming language that is well-suited for artificial intelligence development due to its flexibility and ability to process symbolic expressions. These expressions can contain both simple data types (such as strings or numbers) and complex structures like lists and arrays. This makes Lisp effective in working with large data sets, performing complex computations and manipulations.
The recursive programming mechanism allows for the creation of powerful algorithms for analyzing and processing significant volumes of information. Additionally, automatic garbage collection enables programmers to delegate memory management to the system, focusing on developing core functionality.
Lisp's dynamic typing allows the interpreter to determine variable types during program execution. This provides high flexibility and makes it easy to work with complex data structures, which is important for artificial intelligence tasks.
Lisp is one of the first AI programming languages to support functional programming. Its simple yet powerful syntax structure encourages experimentation and research in AI, especially in areas that require symbolic expression processing.
Best suited for: Scientific research and experimentation in the field of artificial intelligence, particularly for working with symbolic expressions.
Although Lisp was once an innovative language in artificial intelligence, over time it has given way to statistical machine learning and neural networks, which currently dominate the field. However, it remains indispensable for developing expert systems and logical decision-making models based on symbolic reasoning rather than data analysis.
Prolog
Prolog is known for its capabilities in logical programming and symbolic reasoning, making it an effective tool for creating AI solutions, particularly for knowledge representation and solving complex problems.
Thanks to its declarative approach, developers can describe the logic of computations without focusing on the details of execution flow management. This allows for easy specification of rules, relationships, and facts, which is especially useful when creating knowledge bases in artificial intelligence.
The powerful pattern-matching mechanism enables quick analysis of large amounts of information and finding matches based on logical relationships. This makes Prolog a valuable tool in areas such as natural language processing (NLP), helping systems interpret and respond to queries. Additionally, this feature supports the development of various algorithmic solutions.
Best suited for: Symbolic reasoning, knowledge representation, and solving logical problems in AI.
Weakness: Limited usage and a small number of libraries.
What Is the Most Popular Programming Language Used in AI in 2025?
Considering the top programming languages for artificial intelligence, it is worth noting that in 2025, Python remains the most popular. Its popularity is driven by its easy-to-learn syntax and the wide range of libraries and frameworks, such as TensorFlow, PyTorch, and Keras, which significantly simplify the AI development process.
In addition to Python, other languages are also actively used in AI. These include R, known for its powerful statistical analysis and data visualization tools; Java, which offers high performance and scalability; C++, which provides low-level control over hardware; and Julia, which is quickly gaining popularity for its efficiency in numerical computations.
The choice of programming language for AI depends on the specific requirements of the project, available tools, and the personal preferences of the developer.
The Programming Languages That Are Not Recommended for AI
Along with the extensive list of languages for AI development, there are also those that are better used for solving other problems. These are usually niche and low-level languages, such as PHP and Ruby (scripting languages that do not have the needed flexibility and speed to simplify the development process), C (a low-level language that lacks almost all the functions needed in AI development), and Fortran (despite its use for mostly scientific purposes, it has poor functionality).
Ultimately, a seasoned software engineer will be able to cope with any AI development task using any language; another thing is that, given the expected scope of work, the languages that we described in the previous paragraph are much better suited for these tasks.
If you are looking for an experienced team that will launch the digital transformation of your business processes through custom-made AI and ML solutions, feel free to contact us.
Choosing the Best Programming Language for AI Development
From Python to Scala, different programming languages can be used for AI projects. While they share some similarities, each one has unique characteristics that make it optimal for specific tasks.
Here are a few steps that will help you choose the best programming language for your next AI project:
For Your Project
Start by assessing the requirements and needs of your AI project. Pay attention to the structure of the application and the main features it needs to function properly.
Determine whether you are building a web application, a deep learning model, or a robotics project. Different types of projects may require different programming languages.
For example, JavaScript may be suitable for building web applications, such as those related to trading, as it supports many browser features by default. In contrast, C++ would be the optimal choice for robotics development, as it provides low-level hardware integration.
To Learn
Consider your skill level, as programming languages have different learning curves. For instance, Python has a gentle learning curve thanks to its simple and understandable syntax. On the other hand, C++ has a steeper learning curve, meaning it requires more effort to master.
By taking the time to carefully assess these factors, rather than rushing, you will lay a solid foundation for your AI project. Proper planning at the start will help avoid costly problems in the future. Additionally, it will increase your chances of choosing a programming language that best suits both your current needs and long-term goals.
Perhaps you are one of those people who believe that any experienced software engineer can be involved in any project. But in reality, it doesn’t work like that: depending on your task, you may need different expertise from the core members of your development team. Below, we will highlight existing types of software development and briefly consider their specifics.
Types of Software Development
The division of software development into types primarily depends on the platforms for which the created project is intended, that is, the user environment where it will be deployed. This could be a web browser, cloud, mobile, desktop, IoT, or embedded operating system. There are also platform-independent solutions that can be run on several operating systems without any restrictions.
Ultimately, the deployment environment turns out to be one of the main factors influencing the choice of the project’s technology stack, and, therefore, the developers themselves, who have the appropriate expertise.
Web Development
Web development involves creating different types of software—web applications and websites—that run through an Internet browser. Along with this, you have to understand that to implement your web solution, you will need several specialists at once whose scope of work will be concentrated within the following subtypes of web development.
Frontend Development
Frontend development concerns the creation of that part of the project with which the end user directly interacts. In particular, this consists of creating a user interface as well as functionality that is performed without the participation of a server and database.
Backend Development
Backend development lies in the implementation of the business logic of the project and its connections with the database/databases. This is its server part, which is hidden from the eyes of end users.
Full-Stack Development
Full-stack development involves a combination of frontend and backend development, but unlike them, it requires the participation of more general specialists who are well-versed in both server and browser technologies.
Mobile App Development
Mobile app development is aimed at the implementation of projects for mobile operating systems—usually iOS and Android. At the same time, there are also cross-platform projects that work equally well regardless of what OS is installed on the end user’s device.
iOS App Development
iOS app development involves creating applications completely tailored to work on the iOS operating system. Such projects require developers to have knowledge of Objective-C, Swift, and Java programming languages, as well as platform-native technologies and tools.
Android App Development
Similarly, Android app development is aimed at projects tailored to the specs of the Android mobile OS. To do this, developers need to have strong programming skills in Java and Kotlin, and be able to work with the Android Toolkit.
Cross-Platform App Development
Cross-platform development allows developers to cover several mobile platforms at once. In this case, they create a universal code base based on a single technology stack, regardless of which OS will run on user devices. From a business perspective, cross-platform projects allow you to achieve faster launch and lower budget since instead of several separate applications and teams that will work on them, you can hire just one that will create a software solution that can be simultaneously deployed on numerous platforms.
Embedded Software Development
Embedded software development involves creating solutions to control machines or devices that are more primitive than computers, called embedded systems. This software development model is completely tailored to the specific hardware on which the software solution runs and has significant restrictions on the consumption of its resources.
Desktop Application Development
Desktop application software development consists of creating applications that are used to work offline on user PCs and laptops. Instead of applying the computing power of servers on the hosting provider's side, they rely entirely on the resources of the user device on which they are installed.
DevOps and Continuous Integration/Continuous Deployment (CI/CD)
DevOps is a set of interaction practices between developers, testers, and other IT specialists in a project team needed to increase their performance and improve the quality of the work results. Typically, DevOps is applied to launch new versions of software regularly. In turn, CI/CD is one of the DeOps techniques, which consists of automating the software development process to ensure a recurrent development life cycle with a continuous delivery of the program code.
IoT (Internet of Things) Development
IoT development, also called M2M development, is aimed at creating software for systems where devices exchange data with each other without human intervention. These could be physical security systems, smart home and smart city solutions, logistics systems, and so on.
Cloud Development
Cloud development involves creating software that is run on a remote web server using a subscription-based delivery model. From the point of view of end users, this approach ensures the high availability of such software products since they only need to have access to the Internet.
Augmented Reality (AR) and Virtual Reality (VR) Development
While the development of augmented reality solutions involves using the camera of a smartphone or tablet on the user's device (this is necessary to overlay digital objects in the user's real environment), virtual reality solutions also need the use of special wearable devices, such as VR glasses.
Blockchain Software Development
Blockchain software development involves the creation of decentralized platforms, consensus protocols, smart contracts, and other blockchain-related solutions. Although this type of development became popular thanks to crypto projects, today, it covers a long list of niches, from logistics and healthcare to insurance and finances.
API Development
API development is the creation of software tools that allow independent software components to interact with each other using a set of definitions and protocols. Typically, APIs are used to integrate systems, services, and applications to ensure seamless and secure data exchange between them.
Choosing the Right Development Type
At the very beginning of the article, we already indicated that the deployment environment is the main factor influencing the types of software products that you should focus on creating. However, there are also a number of other aspects that will help you make the correct choice. These may include the following:
Project needs and objectives—they will help your team to define not only the tech stack but also the most suitable software development methodologies (usually, they are related to Agile)
Your business niche—it can be finance, healthcare, entertainment, e-commerce, industrial, education, logistics and supply chain, or something else
The resources you have—the specialists you’ve already hired, the project budget, access to data that will operate your software solution, etc.
As for small projects, they are usually dedicated to web development, mobile development, or desktop development—in such software development types, the talent pool is usually wide, and you will be able to choose specialists with the optimal ratio between expertise and rates. However, don't forget that choosing development services that are too cheap may mean you end up having to redo the project from scratch because it doesn't meet your expectations.
Trends in Software Development
Finally, let's take a brief look at the technologies that will be trending in the coming years and which ultimately can provide a strong competitive advantage to your project:
Artificial intelligence and machine learning—today, these technologies find a wide variety of applications, from personalized search results and chatbots to smart analytics and forecasting
Augmented and virtual reality—we have already mentioned these two trends above, so we would like to emphasize that their implementation becomes more and more affordable to project owners
Internet of Things—we also talked about this trend above; its relevance is explained by the possibility of on-site data collection from devices to which the user does not have physical access
Personalized user experience—in general, it can be achieved not only with the help of machine learning but also through predefined algorithms
Blockchain—it’s needless to say that the number of blockchain software development projects is growing every day, and currently, they cover not only the crypto industry
Conclusion
Now, after reading this article, you will be able to understand what type of project you can classify as and what specialists you will need in your project team. At the same time, whichever of the above-described examples of software development you choose, you can always contact us, and we are guaranteed to implement your business idea according to your requirements.
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