Machine learning games are making a notable impact in the market. Wondering how to create such a game? Read on, as we explain the steps.
1. Create a leadership team and kick start the machine learning game development project
You need to initiate the project. Create a small leadership team for that.
Onboard a project manager (PM). Look for experience in managing machine learning (ML) development projects.
Onboard a software architect with artificial intelligence (AI) and ML development experience. Hire business analysts (BAs) that have worked in AI and ML projects.
The PM should kick-start the project. You need the PM to define the project and plan it.
The architect should create technical solutions. Furthermore, the architect should define the non-functional requirements (NFRs). BAs should gather and document the business requirements. You need the PM to oversee these tasks.
2. Gather, analyze, document, and manage the machine learning game requirements
You need the BAs to gather the functional requirements. These pertain to the functional features of games. BAs might need to do one or more of the following to gather the business requirements:
- Interviewing the business stakeholders in your company;
- Interviewing potential end-users;
- Conducting market surveys;
- Reviewing market research reports;
- Analyzing the trends in the gaming sector.
You need the architect to define the non-functional requirements (NFRs) for the proposed ML game. These include performance, scalability, security, maintainability, etc. Architects might need to study industry benchmarks for this.
BAs and the architect should document the requirements. The PM should implement a requirements review process.
You also need the PM to implement a requirements management process. The PM should set up a change management process so that you can avoid scope creep.
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3. Finalize the requirements for the MVP of the proposed machine learning game
You need to make a mark in the gaming sector, which is highly competitive. You need great gaming ideas for that. Furthermore, you need to create a highly engaging ML game. Getting to that stage typically requires multiple iterations.
You might need to launch an MVP (Minimum Viable Product) first. An MVP offers fewer features, however, it’s a functional product. You can develop and launch it quickly. Therefore, you can get market feedback quickly. This helps to validate your ideas.
Identify the most impactful features. Plan to include them in the MVP.
4. Plan the technology stack for the ML game development project
Decide on the technology stack. You need to do that for the following:
A. Cloud computing platform
Choose the kind of cloud platform you need. You could need an IaaS (Infrastructure-as-a-Service) platform. The cloud provider manages the infrastructure here, and you manage the rest. An example is AWS EC2.
Alternatively, you might need a PaaS (Platform-as-a-Service) platform like AWS Elastic Beanstalk. Cloud providers manage the infrastructure, operating system, middleware, runtime environment, and services. You can focus on software development.
For mobile development, you could choose an MBaaS (Mobile-Backend-as-a-Service) platform like AWS Amplify. Such platforms take care of infrastructure and persistent storage. You can build and manage the mobile back-end easily with them.
Choose a cloud provider. Explore providers like AWS, Google Cloud Platform, Microsoft Azure, etc.
B. Game engines
Depending on your project requirements, you might choose to use a game engine. They include excellent tools for game development. You can considerably expedite your project by using them. Explore the following game engines:
- Unreal Engine;
- Amazon Lumberyard;
- GameMaker: Studio;
C. Web technologies
D. Mobile technologies
You can develop either native or cross-platform mobile apps. We recommend you create native apps. Native apps offer the best user experience, performance, and security.
Cross-platform development costs less. However, cross-platform apps can’t quite match the user experience, performance, and security of native apps.
You can use either Java or Kotlin for native Android development. Both are feature-rich languages that offer performance, security, and productivity. We recommend you use Java since it’s more popular.
Use Swift for native iOS development. This modern and powerful language offers excellent features. Swift offers productivity, security, performance, and maintainability. It’s more popular than Objective-C, the older language used for native iOS development.
You can use one of the many open-source SQL databases if you need a relational database management system (RDBMS). We recommend either MySQL or PostgreSQL. Both of them offer robust querying and other capabilities.
Use MongoDB or Apache Cassandra if you need a NoSQL database. These open-source NoSQL databases are highly popular, performant, and scalable.
F. API development
You will likely develop your own APIs for the core functional features. You can use the REST (Representational State Transfer), a popular standard for API development. Many developers already know about REST API development.
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Alternatively, you can use GraphQL for API development. This powerful API query and manipulation language helps you to develop more efficient APIs. GraphQL is relatively new though.
G. Artificial intelligence and machine learning development technologies
AI is a vast field. ML is a subset of it, however, it’s a vast field too. You choose an AI/ML development technology stack based on the AI/ML capabilities you use.
E.g., you could use AI capabilities like computer vision, natural language processing (NLP), etc. Furthermore, you can use different kinds of machine learning algorithms like supervised learning, reinforcement learning, etc.
You could utilize deep learning when developing an ML game. In that case, you might use artificial neural networks, deep reinforcement learning, etc.
The complexity of AI/ML in your game will also influence the tech stack you choose. E.g., you can use ML/AI for the following:
- Procedural generation;
- Game AI;
- Simple learning AI;
- Real-time play complex AI.
How you use ML in your game is a key question. E.g., you could use ML to create realistic interactions or model complex systems. The usage pattern will drive your choice of AI/ML development tech stack.
Programming languages for AI/ML development
You can use programming languages like Python and Java for ML development. Julia is a relatively new language, and it’s a good choice for AI/ML development. However, we recommend you use Python.
Python offers excellent libraries like Scikit-Learn for AI/ML development. You can use these libraries to implement the ML algorithm of your choice. You don’t need to develop the algorithm from scratch. This helps to expedite your project.
Python is one of the most popular languages. Many developers have Python skills, therefore, you can find developers relatively easily.
AI/ML development platforms
You have considerable work in an ML game development project. This includes gathering training data and preparing it. You need to store and manage the data effectively. Significant effort goes into training ML algorithms and testing ML models.
You can use a cloud-based AI/ML development platform to manage and expedite these tasks. Explore machine learning development tools like Azure Machine Learning, Google Vertex AI Platform, Amazon Machine Learning, etc.
H. 3rd party APIs/SDKs
You might use one or more 3rd party APIs and SDKs for non-core features. Explore 3rd party APIs and choose robust ones. E.g., you might use the Stripe API for payment gateway integration.
Another example is the Firebase Cloud Messaging API for push notifications in Android apps. For iOS apps, you can use the Apple Push Notifications service (APNs) for push notifications.
5. Plan the project
You need the PM to prepare an effective project plan. The PM should choose a suitable software development methodology. We recommend the agile methodology for this project since it suits iterative development.
You could use the scrum framework. Your team will deliver short iterations called “sprints”. You should deliver a functional ML game in a sprint. Choose the features that should go in a sprint. Subsequently, estimate and schedule the sprints.
ML development projects have several unique tasks. You need to plan for these. These are as follows:
- Learning about ML algorithms and choosing one;
- Collecting data for training and testing;
- Data preparation;
- Organizing the data sets in the appropriate format;
- Improving data quality;
- Feature engineering after analyzing the input variables in the input data;
- Spitting data sets into training and test data;
- Securing data to keep hackers away;
- Implementing the ML algorithms into your project;
- Testing the ML algorithms.
Ensure that the project plan covers all of the above.
You need the PM to cover the following in the project plan too:
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- Task dependencies;
- Estimating the tasks;
- Project scheduling;
- Project budgeting;
- Planning the technical environment for the project.
- Quality management;
- Metrics management;
- Stakeholder management;
- Human resource management;
- Communications management;
- Stakeholder management;
- Risk management.
6. Hire a software development team
You now need to hire your software development team. Hire competent people for the following roles:
- UI (user interface) designers;
- Game developers;
- Web developers;
- Mobile developers;
- AI/ML developers;
- DevOps engineers.
Choose the right hiring approach. It takes considerable expertise to develop ML games, and such projects are complex. Tailor your hiring approach accordingly.
You might think of hiring freelance developers. While you might get freelancers at a low hourly rate, this approach is risky.
Freelancers work part-time on your project. You might not get enough of their time. Freelancers might leave your project mid-way, and you need to look for replacements. Freelance platforms don’t offer any management support.
Hire developers from hybrid software development companies like DevTeam.Space. Such companies provide full-time, skilled, experienced, and motivated software engineers.
Companies like DevTeam.Space provide management support. You can get replacements from them if developers leave the project mid-way. This hiring approach suits your complex project.
We recommend you use the scrum framework to manage this project. Build small, cross-functional scrum teams. E.g., you can bring separate scrum teams for web development, Android development, iOS development, and ML development.
Scrum masters should lead these teams. You should have UI designers, developers, and testers work together in scrum teams. Scrum teams should work closely with the product owner.
Foster a climate where the team focused on client value. Encourage collaboration and openness. Scrum teams should be empowered enough to estimate and schedule features in sprints.
The teams should have daily stand-up meetings to discuss project status and resolve issues. Scrum teams should demonstrate the ML game to product owners in sprint review meetings. You should get the sprint approval in these meetings. Scrum teams should have a lessons-learned exercise named the “sprint retrospective” after a sprint.
8. Execute, monitor, and control the AI/ML game development project
Your PM should lead the project execution. Subsequently, the PM should monitor and control the project. The following key activities need focus:
- UI design;
- API development;
- Web development;
- Android development;
- iOS development;
- Implementing, training, and testing the ML algorithms;
- Reviewing the ML models;
- Reviewing the training and testing data quality and enhancing them;
- Code review;
- Testing, which includes unit testing, integration testing, performance testing, security testing, etc.;
- Post-deployment and warranty support.
You should also monitor how the MVP performs and plan subsequent enhancements.
Planning to create a machine learning game? Contact DevTeam.Space to hire smart developers.
Atari, Doom, Minecraft, StarCraft, and Car Racing are some of the well-known games that use machine learning. Computerized versions of ancient games like chess and Go also utilize ML.
The top Python machine learning development libraries are TensorFlow, PyTorch, Keras, Orange3, NumPy, SciPy, Scikit-Learn, Pandas, Matplotlib, and Theano.
The most popular ML algorithms are Linear Regression, Logistic Regression, Decision Tree, Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbors (KNN), K-Means, Random Forest Algorithm, Dimensionality Reduction Algorithms, and Gradient Boosting Algorithm.