Step-by-Step Guide to Custom AI Development

Custom AI development takes more than fancy programming languages like Python and C++. While it is important to be able to design an algorithm and translate that design into a working algorithm, there are plenty of other factors to consider for solving a carefully articulated problem. 

AI development companies like Debut Infotech Pvt Ltd, with years of experience in AI development, know what it takes. We know that the only way to create truly helpful solutions is to take a holistic approach to custom AI development that begins and ends with fixating on the problem. 

In this guide, we teach you how to build a customizable AI solution from scratch — from problem identification to data gathering to deployment and optimisation — with some best practices on how to make the right choices in the custom AI development process. 

What is Custom AI Development? 

Custom AI development is a special process of building a highly specialised and customizable AI solution in order to solve an organization-specific problem. The custom AI solutions built using these approaches serve only the unique needs they were created for and struggle to fit into other use cases without explicit customisations by skilled AI development companies.  

It’s just like the process of making bespoke clothing. These clothing items are made to fit the exact body shape of one person, and another person would find it difficult to fit in them without making adjustments. 

Off-the-shelf AI development is the direct opposite of custom AI development. This approach leads to the creation of out-of-the-box (OOTB) AI solutions that are pre-packaged and sold to cater to the needs of different audiences regardless of their unique peculiarities. 

Debut Infotech’s Tailored Methodology for Custom AI Development 

You can’t apply a one-size-fits-all approach when creating custom AI models. Excellent execution requires immense technical expertise, careful planning, and an accurate understanding of what needs to be done. 

To nail this perfectly, the best AI development services, like Debut Infotech Pvt Ltd, follow the steps outlined below. 

You can too! 

1. Understanding Your Needs 

Firstly, you need to clarify the problems you’re trying to solve. This means deciding the AI model’s purpose and the different applications your target audience will need it for. 

Most companies build custom AI solutions for many reasons, depending on their organisational needs. However, the following are some of the most important and popular reasons: 

• Customer service enhancements and automation 
• Predictive analytics and forecasting 
• Workflow automation
• Fraud detection and security enhancement 
• Health monitoring and diagnostics 
• Text, image, or video creation 

These different needs determine the type of custom AI solutions you’ll be building and the data requirements for such projects, literally setting the tone for the rest of the project. At Debut Infotech Pvt Ltd, we collaborate with project stakeholders and even target audience representatives, if need be, to identify a business’s strategic goals and existing technical infrastructure. During this collaboration, we look out for a couple of things, such as: 

1. Feasibility: This means the realistic chances of AI solving the problem at hand. We check for this by assessing the capabilities of AI solutions in that field that have already solved this or a related problem. 
2. Impact: This involves quantifying the potential positive impact that such custom AI solutions can bring to the target audience and the organisation as a whole.  
3. Data Availability: Finally, we confirm if the business has access to the kind of high-quality data addressing the specific problem and is required to train and build the custom AI models. 

This way, we ensure the resulting AI solution is not just technical but specifically solves the problem at hand. 

2. Assessing Your Data Requirements 

Data is the lifeblood of AI models. They determine the output, efficiency, and effectiveness of any custom AI solution. Therefore, following the data availability requirement we assessed for in the first step, you need to identify the high-quality data that your custom artificial intelligence solutions need to learn from. 

Next, you have to gather and prepare the high-quality data whose patterns you want your AI models to reflect. In response to the rising ethical AI concerns, this is a very crucial stage in your custom AI development process. Therefore, you must follow the principles of responsible AI. 

But where and how do you get the right data? 

Potential data sources include the following: 

• Internal databases, i.e., data generated by your organisation
• Publicly available dataset reservoirs 
• Data marketplaces 
• Operational data, e.g., machine metrics, sensor data, and supply chain data
• Transactional data, e.g., past sales data and browsing history

Many more data sources are depending on the custom AI solution you’re building. 

So, how do you gather these data? 

The following are some relevant and effective data collection methods for building custom AI solutions: 

• Crowdsourcing: Gathering data from a large quantity of people via surveys and feedback. 
• In-house data collection: Collecting data privately within an organisation.
• Automated data collection: Using web scraping tools, APIs, and web crawling to scour the internet

Finally, after gathering the required data, you must prepare it for effective model training. This means thoroughly analysing and cleaning the collected data to keep them clean and structured for effective model training. The process of cleaning and preprocessing involves removing duplicate entries, handling or eliminating missing values, scaling numerical features, and fixing errors and typos. 

3. Developing a Custom Strategy 

The process of developing a custom strategy is where things start to get highly “customized.” At this point, you start choosing the methodologies, technologies, tools, and frameworks that you’ll use to build your custom AI solution. Furthermore, you should also think about and decide on the deployment solutions you’re going to apply. 

Doing this not only saves you time and resources but also ensures that you’re building an AI solution that effectively and efficiently solves the problem you identified at the beginning. 

For instance, at Debut Infotech Pvt Ltd, we develop a wide range of custom AI solutions for different businesses using the following AI frameworks. 

• PyTorch
• Keras
• Scikit-learn
• Tensorflow
• Hugging Face Transformers 
• NLTK

When it comes to programming languages for writing the actual code, we use some of the following: 

• Python
• R
• C#
• Java
• C++ 

Finally, when it comes to deployment, we consider different options depending on your needs. For instance, we first weigh the most appropriate approach between on-premise and cloud hosting. We also use edge computing for AI applications that require real-time processing. However, most businesses nowadays prefer to deploy to the cloud, so we use the following common cloud deployment services: 

• Amazon Web Services (AWS) 
• Google Cloud 
• Azure

The perfect combination of technologies, AI frameworks, and deployment approach plays a pivotal role in launching the perfect artificial intelligence technology for your business

4. Design and Development 

After gathering the high-quality data you need and selecting the perfect technologies, you can design your algorithms and build the exact models tailored to your problems. However, you still have to decide on the category of algorithms that will be powering your customizable AI solutions. The broad classes are: 

• Supervised learning algorithms: These are algorithms that identify patterns and predict outcomes based on labeled datasets
• Unsupervised learning algorithms: These are algorithms that analyze unlabeled data to identify patterns, structures, and relationships and predict outcomes.

Once you choose the class that suits your unique problem, you need to define the corresponding architecture and parameters. These choices decide how your AI agent functions and how it is structured. Some common architectures for AI model design include: 

• Modular design: A modular design approach involves dividing the entire custom AI solution into “modules” or “components” and building each module separately before integrating them. Breaking the entire model into modules makes it easier to build, debug, update, and scale the AI agent. This is because making changes to one module doesn’t necessarily affect the other modules. 
  
• Concurrent design: A concurrent design approach involves executing multiple tasks simultaneously. Since the tasks are executed in parallel in this approach, you utilise your resources efficiently. As such, it is ideal for real-time applications like managing multiple conversations at once, where speed and efficiency are crucial. 

When making these decisions, you should consider some vital factors like the problem’s complexity and data volume. 

Here’s why: 

Established algorithms like decision trees may be perfect for simple problems. However, some unique problems might require you to hire AI developers capable of conceptualizing a problem and developing unique AI algorithms from scratch. Moreover, some models, like deep learning models, need a large amount of data for effective training. So, you need to be sure you have enough data for training when choosing such algorithms. 

Remember, everything you’re building is “custom”,; so you have to think about every choice that shapes your outcome. That’s the beauty of it all! 

5. Training and Optimization

By now, your AI models are already built. This means the code has been written, and it’s now time to teach your AI system to perform the specific task you want. The training and optimisation process often involves a lot of experimentation and fine-tuning. This is because you’ll be feeding your built AI model the data you gathered and prepared in step two and gauging its output. So, you must keep an open mind through it all. 

When configuring your training process, you have to use your previously cleaned and pre-processed data to approach the identified problem. Some common training algorithms include: 

• Decision trees
• Support vector machines (SVM)
• Neural networks 
• Linear regression
• Logistic regression
• K-means clustering
• Gradient boosting

There are quite a number of these algorithms for both supervised, unsupervised, and reinforcement learning solutions. So, only highly technical AI consulting firms like Debut Infotech Pvt Ltd can identify the right fit. 

In addition to selecting the right training algorithms, there are some other considerations to prioritise when training and optimising your algorithm. Some of these include: 

• Hyperparameters: These are external configuration variables that control the learning process. They include subtle settings like the batch size, learning rate, model architecture, number of epochs, and the number of hidden layers or units. 

These settings take on different values, and you’ll experiment with them until you find the optimal choice during your training process. 

• Loss function: The loss function tells you how accurate your AI model is by evaluating the difference between the real values and your model’s prediction. The purpose of the training and optimisation process is to reduce the loss function value. 

All these things take time, as training is an iterative process. So, feel free to experiment and optimise accordingly till you find the optimal settings required to make your AI solutions fit your unique needs. 

6. Deployment and Support 

This is the part where you put your trained algorithms into a production environment for your target users to access them and integrate them into their applications. If you followed the steps accordingly, you should already know how you plan to deploy your custom AI solutions. Nonetheless, some common approaches include: 

• On-premise deployment: This means installing and operating the built AI system on the company’s servers, which are managed by the company’s in-house IT team. 
  
• Cloud deployment: Cloud deployment involves hosting and managing the AI system on third-party providers. This is usually easier than on-premise deployment and offers a scalable infrastructure for managing model versions. Major providers include AWS, Azure, and Google Cloud. 
  
• Edge deployment: This means running the custom AI solutions directly on devices at the edge of a network, such as smartphones, IoT devices, or autonomous vehicles. This deployment approach allows the AI model to work offline while also reducing latency. However, it often has limited resources.  

Your deployment approach should be carefully selected to ensure that the model can handle the increased workload. Furthermore, you must ensure that there are enough provisions to protect sensitive user information to satisfy all applicable regulatory requirements. 

Yet, the work is not finally complete after deployment.

AI models need constant attention and monitoring to ensure that they stay relevant, effective, and accurate, even after deployment. Therefore, you must regularly update these systems and follow appropriate maintenance best practices to guarantee optimum performance. Some of these best practices include: 

• Regularly tracking model performance. 
• Keeping the training data clean, relevant, and updated.
• Regularly testing the custom AI solution and its components
• Regularly running security updates. 
• Maintaining detailed documentation of data sources, algorithms used, and performance metrics

By following these deployment and maintenance best practices, your target users will be able to enjoy your custom AI solutions for a sustained period of time. 

Conclusion

As you launch your customizable AI solutions, remember that custom AI development is all about the unique problem you want your AI solution to solve. The perfect custom AI solution addresses the needs of its target user base and caters to the preferred AI use cases. 

The customizable AI solutions that Debut Infotech has built for brands like Lummid Solutions, TechSpeak Innovations, and TalentQuest Innovations are all thriving because we aggressively focused on their problems at every development phase. Once we identified the problem, we gathered relevant high-quality data and developed a custom strategy. Next, we designed and developed our model before training and optimizing it. And in no time, we deployed them for use! 

With our AI development services, you can also launch your custom AI solutions using the same processes today! 

Frequently Asked Questions (FAQs)