The artificial intelligence market is growing like never before, anticipated to surge from $241.8 billion in 2023 to nearly $740 billion by 2030, with a projected compound annual growth rate of 17.3%, according to Statista. The global love for AI tech is also exploding, with major trends like chatbots, image-generating AI, and mobile applications set further to enhance the AI landscape in the upcoming years.
Since the booming of artificial intelligence, companies have been racing to craft mind-blowing chatbots, AI solutions, and NLP applications. But how do they pull off these feats?
Well, the magic lies in the AI development process, from defining the problem to deploying the solution and many more stages in between.
In this article, we will walk you through every detailed step you need to take to successfully create your own AI software, leaving no stone unturned.
What Is AI Development?
AI development is the intriguing process of teaching machines to think, learn, and do tasks that usually need human intelligence, all without direct human involvement. This includes creating algorithms, data models, and structures that allow machines to imitate human cognitive abilities like understanding speech, analyzing images, and making decisions.
Nowadays, AI development helps companies make excellent NLP applications, like chatbots with human-like conversations or voice assistants handling your daily tasks. The aim is to make machines smarter and more effective to support us in solving tricky problems and improving our daily lives.
However, it’s no simple task! AI development involves a lot of data analysis, training, and experimentation.
Now, let us explore what a successful AI software development process looks like – step by step!
Successful AI Software Development Process Step-by-step
1. Defining Objectives and Requirements
The first thing to do when developing AI software is to clearly outline the goals of your AI system, whether it’s enhancing customer service, improving efficiency, or addressing complex problems.
To define the scope effectively, you should have a deep understanding of the domain where the AI application will operate, as well as the preferences and needs of end users. It’s also important to collaborate with business stakeholders, domain experts, and data scientists to ensure the end goal is well-defined, attainable, and aligned with organizational strategic objectives. A precise description of the problem in this stage will facilitate the following steps: data gathering, model creation, and testing.
In this phase, you also need to do resource allocation planning for a smooth development process later on. The complexity of the project will determine the number of resources required, including computational and human resources like data scientists and AI developers.
Additionally, at this stage, you should determine specific data sets required for analysis. For example, if the goal is to develop an AI-powered recommendation engine for an e-commerce website, you should identify essential customer data such as purchase history, browsing behavior, and demographics.
2. Gathering Data
Next is the collection of training data, a crucial step in an AI software development project since data fuel a machine learning model. Yet, obtaining relevant and high-quality data is often challenging and remains a barrier in most AI adoption projects.
Typically, you will deal with two types of data:
- Structured data: This includes organized data found in databases and Excel spreadsheets.
- Unstructured: This encompasses text, images, audio, video, or any other data not organized in a pre-defined structure.
The sources for your relevant data vary based on the nature of your AI software. While some businesses opt for AI data collection services, you can collect your own data through scraping tools, utilize pre-packaged data, or employ crowdsourcing.
3. Data Preparation and Manipulation
Following data collection, it’s time to refine the data set to align it more closely with the AI project’s objectives.
3.1. Data Quality and Cleaning
This stage primarily requires data preprocessing to ensure its quality while being collected. Poor-quality data can significantly impede model performance, so it’s vital to perform data cleaning to eliminate any irrelevant or duplicate data, as well as manage missing values. Consider using some tools to help with data preparation and cleaning, such as Trifacta, Paxata, and DataRobot.
3.2. Transforming Raw Data
This step, also known as post-processing of training data, entails converting raw data into a format suitable for machine learning models. This is where data manipulation techniques come in handy, with data scientists usually conducting data analysis to transform raw data into relevant features.
3.3. Feature Selection
Next is identifying the most relevant variables or features from the data set for AI algorithms to recognize patterns and anomalies. This is typically done using different techniques such as filter, wrapper, and embedded methods.
- Filter Methods
Choose features based on their statistical properties, like their correlation with the target variable. These methods are computationally efficient and offer a quick way to identify the most relevant features in a dataset.
- Wrapper Methods
Select features based on their capacity to enhance model performance. Despite being computationally more demanding than filter methods, they can deliver more precise results.
- Embedded Methods
Incorporate feature selection into the model training process. These methods strike a balance, offering both computational efficiency and accurate results. However, it’s important to note that they may not be universally suitable for all models.
3.4. Data Annotation
At this step, your job is to annotate data to render the machine readable. Data annotation combines labeling data with relevant tags to facilitate computers in comprehending and interpreting the information. This data may take the form of images, text, audio, or video, and it’s the responsibility of data annotators to label it accurately. Annotation can be accomplished manually by humans or automatically through advanced machine-learning tools and algorithms.
Annotated data serves as the foundation for supervised learning models. This is because machines cannot interpret images and videos like humans, and data annotation makes diverse data types comprehensible for machines. This role becomes particularly crucial in sensitive fields like healthcare, where errors in AI/ML models can pose huge risks.
Various data annotation techniques are available, including:
- Reinforcement Learning with Human Feedback (RLHF)
This method improves the learning process of an agent (RL algorithm) by incorporating human feedback into the learning loop. Instead of solely relying on a programmer-defined reward function, RLHF leverages human intelligence to guide the learning process. The agent learns not only from the consequences of its actions but also from corrective human feedback, pointing out errors, or affirmative feedback reinforcing correct decisions.
- Text Annotation
This technique trains machines to comprehend and interpret textual information. For instance, in chatbots, text annotation enables machines to identify user requests based on keywords and provide appropriate solutions. There are different types of text annotation, such as:
– Semantic annotation: Tagging text documents with relevant concepts makes it easier to discover unstructured content by associating it with meaningful tags.
– Intent annotation: Examining the underlying purposes behind texts and categorizing them based on intentions such as requests and approvals.
– Sentiment annotation: Tagging emotions expressed within texts, aiding machines in identifying human emotions conveyed through words.
- Text categorization
Assigning categories to sentences or entire paragraphs in alignment with their subject matter.
- Image annotation
Labeling images to train AI or ML models. There are four fundamental types:
- Image classification: Train machines to determine what an image represents based on annotated examples.
- Object recognition/detection: An advanced image classification version that accurately describes the numbers and precise positions of entities in an image.
- Segmentation: A more advanced form of image annotation, this means dividing images into many segments to facilitate analysis, with each segment representing an image object.
- Video annotation
Teaching computers to recognize objects in videos. Both image and video annotations are methods of data annotation used to train computer vision (CV) systems, a subfield of artificial intelligence.
- Audio annotation
Classifying components within audio data. Like other annotation types (such as image and text), audio annotation requires manual labeling and specialized software.
4. Model Selection and Development
The next step is selecting the appropriate AI model to address the specific problem you’re trying to solve in the first place.
4.1. Choosing the Right Algorithms
Machine learning offers a variety of algorithms tailored to address different tasks. Among them, 3 primary types of machine learning algorithms are employed to build ML models:
- Supervised learning algorithms
Predict a target or outcome variable from a set of predictors. The model is trained until it achieves the desired accuracy on the training data.
Examples of supervised learning are Regression, Decision Tree, Random Forest, KNN, Logistic Regression, etc.
- Unsupervised learning algorithms
This algorithm has no target or outcome variable to predict. Instead, it is used for tasks like recommendation systems or clustering populations. Clustering algorithms are commonly used to segment customers into different groups for specific interventions.
Apriori algorithm and K-means clustering are good examples of unsupervised learning.
- Reinforcement learning algorithms
These algorithms train machines to make the most accurate decisions possible by exposing them to an environment where they continually learn through trial and error.
By way of Reinforcement Learning examples, we shouldn’t miss the Markov Decision Process, Q-learning, and Monte Carlo Methods.
As you can see, there are numerous machine learning algorithms to choose from. You should carefully pay attention to testing and evaluating different factors:
- Type of task: Is it classification, regression, or clustering?
- Quantity and quality of data: Do you have a large or smaller volume of high-quality data?
- Computational resources: Do you have the computing power to support more complex algorithms?
- Time constraints: How quickly does the model need to be deployed?
4.2. Using pre-trained models
You can also take into account pre-trained models, which can significantly streamline your AI development process. These models have undergone training on extensive datasets and can be adapted for similar tasks. For example, when dealing with image recognition, employing a pre-trained model such as VGG or ResNet can drastically reduce training time.
While pre-trained models offer a solid foundation, it’s essential to note that they may require fine-tuning to cater to your specific requirements. Therefore, you should train the model on your own dataset, aligning its capabilities more closely with the objectives of your project. This strategy combines the strengths of both custom and pre-trained models, ensuring a balance between efficiency and customization.
4.3. Programming Languages and Tools
The choice of programming languages and tools is pivotal in AI software development. Python, R, and Java are among the most widely used programming languages for this purpose. Moreover, deep learning frameworks like TensorFlow and PyTorch are frequently utilized for more complex models.
5. Training the Model
After finalizing your model selection, the next step is training it with your dataset, a pivotal phase in the overall development process.
5.1. The training process
During this phase, your AI model acquires a deep understanding of the tasks it’s designed to perform. This entails feeding your meticulously cleaned and pre-processed data into the model. As the model processes this data, it learns to make predictions based on the information it has assimilated.
Take the context of a sentiment analysis machine learning model as an example. The training process would expose the model to diverse text samples and corresponding sentiment labels. This exposure enables the model to identify patterns in how words and phrases relate to different sentiments.
5.2. Continuous Learning
An inherent capability of AI models is continuous learning, which is crucial in today’s dynamic landscape. As data is consistently generated, AI models can evolve and adapt. By routinely updating the model with new data, you ensure its ongoing relevance and accuracy in predictions and decision-making. This mechanism allows the model to keep pace with evolving patterns and trends in the data it encounters.
6. Validation and Testing
Moving forward is the validation and fine-tuning stage.
6.1. Evaluating model performance
Here you need to validate the AI model by testing its performance on a new, unseen dataset. Data scientists usually use metrics like accuracy, speed, and reliability to gauge the effectiveness of the model.
You may consider employing techniques like cross-validation, A/B testing, and confusion matrices to measure performance and pinpoint areas for enhancement.
Furthermore, this stage might also involve testing the AI system in real-world environments to assess its performance under diverse conditions and user scenarios.
Evaluating the model is crucial to ensure it aligns with desired criteria and user requirements. By identifying and addressing any issues during testing and evaluation, you can increase the accuracy and reliability of the system, delivering a more effective AI application.
6.2. Fine-tuning
If the model doesn’t meet performance metrics, reassessment might be necessary. AI engineers need to gather additional training data or select alternative machine-learning algorithms to improve model performance.
7. Deployment and Maintenance
Finally, deploy the AI model for end-users, which requires careful planning and execution to ensure secure and reliable model deployment.
It means installing the AI application on local servers or cloud technology, configuring security protocols, and integrating the system with other applications and databases.
Once deployed, it is vital to carry out ongoing monitoring and maintenance to adapt to new data and conditions. You also need to make regular updates or modifications to keep the system continuously meeting the ever-changing needs of users.
The above has shown 7 key steps that you should follow to develop AI software. However, please remember that a successful AI software development process necessitates adopting a data-driven and iterative approach. This means continuous data collection and analysis to inform decision-making, ongoing testing and refinement of models, and collaboration across various teams and departments to align the AI solution with business objectives.
Additionally, investing in advanced technologies like cloud computing, big data analytics, and automation is essential for facilitating the swift development and deployment of AI solutions. By adhering to these best practices, you can confidently leverage the power of AI to foster innovation, enhance efficiency, and unlock new growth opportunities for success.
Kickstart your AI solution with Neurond
Embarking on AI development is a multifaceted and iterative undertaking, demanding proficiency across diverse domains, including data science, machine learning, and software engineering.
By adhering to the fundamental steps – from defining objectives and gathering data to preparing, manipulating, selecting, developing, training the model, and ultimately deploying and maintaining the AI software – you can successfully deliver AI solutions that yield substantial business benefits and effectively tackle real-world challenges.
Neurond gives you a helping hand in catching up with the rapid evolution of AI technology! Our AI expertise ranges from machine learning, natural language processing, computer vision, and forecasting to AI-powered labeling, chatbots, recommendation systems, and more. We offer comprehensive consultation services, guiding you from AI strategy development to product launch. We will thoroughly evaluate your existing platform and goals to identify how AI can elevate your business and develop a customized project plan for you, from data preparation to modeling and refinement. Neurond is committed to helping you develop an AI solution that truly matters.
So, contact us today to start your AI revolution journey!