How to Build and Deploy Custom AI Models

An AI model supports data analysts and business professionals in analyzing training data patterns to make insightful predictions in various use cases as a powerful tool for decision-makers and answer-seekers. Regarding real-world applications, businesses leverage this technology to identify fraudulent transaction activities, help medical experts personalize patients’ treatment, and provide marketers with valuable insights into customer journeys.

Building their own model grants enterprises customization flexibility to address all identified problems and deliver value. Preparing high-quality data and choosing the proper training method contribute significantly to the accuracy and relevancy of the prediction results.

The article will outline how the business processes can benefit from investing in building a custom AI model and provide a complete guide to deploying it in reality.

Benefits of a AI Custom Model

Custom AI development helps businesses tailor solutions to their needs, streamline integration processes, and achieve scalable implementations. These benefits result in significant competitive advantages for companies toward the growing market needs.

Here are why businesses should prioritize custom options when choosing their own AI model development approaches.

Better Adaptability with Existing Infrastructure

Custom AI models work well with your existing systems, making it easier to adapt to changes and grow your business. Additionally, they surpass pre-built models in addressing the entire domain of your business needs with more accurate and reliable results.

Regarding online businesses with extensive product lines, ready-to-use AI models perform limitedly in personalized recommendations due to a lack of training on product items and customers’ unique preferences. Meanwhile, custom-built AI models excel at this specific task while being trained on businesses’ first-party data, providing highly effective and personalized recommendations.

Enhanced Operational Efficiency

Custom AI models automate repetitive duties and assist with customer service, freeing up human agents. This practice allows internal employees to dedicate more time to more complex and higher-value tasks.

It can also assist businesses in optimizing supply chain management to boost the entire system’s productivity and reduce costly human error. Specifically, it optimizes inventory levels and automates logistics based on accurate demand prediction from business domain-specific knowledge, leading to faster delivery times and significant cost savings.

Consequently, built-in AI models improve profit margins and businesses’ responsiveness to diverse operational issues.

Improved Generated Content Quality

Custom AI models can generate highly accurate and insightful content tailored to specific business requirements by leveraging data gathered from customer interactions, product information, and internal documentation. This application effectively creates advertisement materials and exports market segment reports for marketing businesses.

Indeed, custom AI solutions can help optimize advertising ROI by creating personalized messages tailored to individual customer segments. This leads to lower cost per customer acquisition and improved conversion rates. Additionally, businesses can generate compelling ad copy and engaging social media content to nurture customer satisfaction, differentiating themselves from competitors.

Better Data Security

Sharing sensitive data to third-party services poses potential risks of unauthorized access or data breaches for businesses when choosing pre-built AI models. Meanwhile, custom AI solutions grant complete control over your data, ensuring it remains within your secure infrastructure. By maintaining data ownership and control, businesses can achieve competitive advantages, enhance customer trust, and comply with relevant data privacy regulations.

Step-by-Step Approach to Develop Own AI Model

Building a custom AI model requires a strategic approach to ensure the model training effectively addresses your business challenges. The following 10-step guide will help organizations develop a custom AI solution tailored to current operational issues and the system infrastructure.

1. Determine Issues and Goals

Before developing an AI model, you must assess the business landscape to identify potential problems that need improvement. Afterward, clarify the outcomes you expect the AI model to achieve and list challenges that might prevent businesses from reaching their goals. This practice will provide a comprehensive roadmap for model building, ensuring alignment with business objectives and maximizing the return on investment.

Businesses should quantify the problem’s impact to provide a specific target for the AI solution. For example, the problem statement could be: “Customer churn has increased by 16% in the last 3 months, resulting in a projected revenue loss of $2 million.” This specificity allows businesses to benchmark the AI model’s performance.

Additionally, businesses should consider challenges regarding data availability, technical complexity, and ethical considerations to avoid costly implementing approaches. Common use cases that custom AI models can support enterprises to include:

• Predictive analytics for business forecasts
• Customer service chatbots
• Image recognition
• Natural language processing (NLP)

2. Choose The Right AI Model

Once outlining the problems and outcomes, move to the next step of selecting the appropriate AI model. Your model must perfectly match the specific business problem and available datasets.

Here are some standard AI models accompanied by their applications for businesses:

• Supervised learning: This approach learns from labeled data, where the desired output is known. Businesses choose this pre-trained model for prediction and classification tasks, such as forecasting sales, estimating project completion times, and segmenting customers.
• Unsupervised learning: This method works with unlabeled data, seeking patterns and structures to discover hidden insights. Regarding real-world applications, this AI model assists businesses in identifying fraudulent transactions or discovering products frequently purchased together.
• Reinforcement learning: The approach is beneficial for dynamic and complex scenarios. Organizations prioritize this AI model for tailoring product suggestions, optimizing warehouse operations, or adjusting real-time prices based on market conditions.
• Neural networks (Deep learning): This model effectively learns complex patterns from diverse data. Most enterprises select this model for tasks involving unstructured data like images, text, and audio. Businesses can implement this language model to handle typical applications such as generating human language for chatbots, recognizing images, or converting spoken language to text for voice assistants.

3. Gather and Prepare Data

The data preparation process requires careful planning and execution, which significantly impacts the performance and effectiveness of the AI model. Based on the chosen AI model, businesses can choose the specific data points relevant to their problem. The data types used to train the AI model encompass images, video, audio, and text data.

Businesses can gather data from the following sources:

• Internal data: CRM systems, data warehouse.
• External data: Public datasets, third-party data providers, market research reports.
• User-generated data: surveys, feedback forms, online reviews.

After collecting all required data, businesses must clean it up to correct errors, inconsistencies, and missing values.

4. Choose an AI Framework or Tool

Developers can use pre-built components and optimized algorithms offered by various AI frameworks and cloud platforms to build the AI model. By saving time to reinvent everything from scratch, businesses can focus on more complex problems, maximizing ROI and minimizing development time.

Here are some key AI frameworks and platforms for business use:

• TensorFlow (Google): A powerful open-source AI platform widely used for machine learning and deep learning.
• PyTorch: Another popular open-source framework, well-suited for businesses engaged in cutting-edge AI research or developing highly customized models.
• Keras: An easy-to-use framework that simplifies the process of building and training neural networks.
• Microsoft Azure AI: Ideal for businesses seeking to quickly integrate AI into existing workflows or handle common tasks like sentiment analysis, image classification, or chatbot development.  

5. Train Models

The next step is training your own AI model after choosing the appropriate framework and preparing all required data. Each stage of this step ensures the model can learn from the data, recognize patterns, and make predictions.

The training process includes:

• Data splitting: This step prevents overfitting while ensuring the model’s reliability and ability to deliver accurate predictions. Developers will divide the dataset into distinct training and testing sets. Specifically, 70-80% of the data will be used to train the data, while the remaining 20-30% will serve as an independent evaluation ground to assess the model’s performance on unseen data.
• Parameter tuning: Fine-tuning model parameters, such as learning rate and batch size, is essential to improve performance. This practice aims to find the parameter combination that generates the best results on the testing set, maximizing accuracy and minimizing errors.
• Model training: The model analyzes and identifies underlying patterns from the training data type. Algorithms and optimization techniques guide the training process to minimize the difference between the model’s predictions and the actual values in the training data.
• Model assessment: This step uses the testing sets to assess the model’s accuracy, reliability, and ability to generalize to new data. It provides insights into the model’s strengths and weaknesses, informing further adjustments and refinements. Following successful assessment, the trained model is deployed as a model endpoint, enabling its integration into applications and services for real-time and batch prediction.

6. Deploy The AI Model

Deployment involves integrating the custom models into a production environment, allowing users to interact with the system. Businesses can incorporate their own models to provide intelligent features for mobile, web apps, and enterprise systems, enhancing users’ experience. Regarding large-scale AI applications, cloud platforms enable scalability and flexibility to accelerate and streamline the development process.

7. Ongoing Maintenance and Improvement

AI model’s parameters require continuous monitoring and updates based on evaluation metrics to deliver ongoing business value and maximize long-term ROI on AI investments. Key strategies for improving AI model performance include gathering user feedback, integration, A/B testing, version control, and regular training. This final step ensures the businesses’s AI custom models remain valuable assets and drive informed decision-making.                    

Challenges of Custom AI Model Development

A custom model can give various industries such as healthcare, finance, retail, and hospitality a competitive edge. However, the deployment process might face significant challenges, leading to inaccurate results and over-budget implementation costs.

Here are the four considerable challenges that organizations need to address during the AI projects:

Data Collection and Quality

Inconsistent, biased, and insufficient data prevents AI models from learning effectively to generate accurate predictions. Using low-quality data sources for custom training limits businesses in making insightful decisions. For example, healthcare services might provide incorrect diagnoses and treatment plans due to poor data analysis.

Model Complexity and Selection

To maximize their performance, highly complex AI models, like the object detection model, require vast data, computational power, and expertise. Choosing these types can make it challenging to manage and update the model quickly to adapt to sudden market shifts.

Computational Resources

Businesses require considerable computational resources, which multiply enormously during the scaling progress for more insights. Consequently, they must invest more in expensive hardware or cloud services that offer powerful processors and plenty of memory to proceed with diverse data types. This fact demonstrates small and medium businesses’ limitations in approaching advanced AI technologies.

Ethical and Regulatory Considerations

Addressing data privacy, algorithmic bias, and transparency builds customer trust, strengthens brand reputation, and mitigates legal and regulatory risks. A clear ethical framework guides responsible AI development and guarantees impactful AI solutions.

Why Choose Neurond As Your Custom AI Model Development Partner?

As the custom AI development process requires various complex technical expertise, working with a reliable partner will shift your business toward success with minimal cost and effort. Neuround is an ideal candidate for your consideration, with multi-year experience in consulting and developing artificial intelligence solutions, especially in the Generative AI sector.

Neurond’s expertise in cutting-edge LLMs and GenAI models drives our solutions to update with the most advanced features. Furthermore, we offer our clients the flexibility to customize the AI models based on their unique requirements, whether for internal use or customer-facing applications. By gaining more insights from user feedback, we can continuously improve the model’s performance for stable scalability.

Experience our proven track records and detailed consultants from the Neurond team to prepare your businesses for the evolving development of AI technology.

Contact us now!