The AI software development life cycle (AI SDLC) refers to the process of building and deploying artificial intelligence-based software applications. It encompasses various stages such as problem definition, data collection and preprocessing, model building and training, testing and validation, deployment, and maintenance. This structured approach ensures the systematic development of AI solutions, from conceptualisation to operationalisation, while adhering to best practices and industry standards. Understanding AI SDLC is crucial for organisations looking to utilise the power of AI to build innovative solutions. This article explores the AI SDLC in detail, providing a structured approach to managing AI projects from conception through deployment.
1. Conceptualisation and Planning
The first step in the AI SDLC is identifying opportunities where AI can provide substantial benefits over traditional methods. This involves understanding the problem space, potential data sources, and the feasibility of AI solutions. Stakeholder meetings and expert consultations are critical during this phase to set realistic expectations and clear objectives.
Once an opportunity is identified, detailed strategic planning begins. This includes outlining the project scope, defining milestones, allocating resources, and establishing a timeline. Additionally, risk assessment is crucial to foresee potential challenges in data acquisition, model training, and deployment phases.
2. Data Acquisition and Preparation
AI systems rely on large volumes of high-quality data. Data acquisition involves collecting data from various sources, which could include internal records, online data repositories, or real-time data streams. Ensuring data relevance and legality is also fundamental during this phase.
Data preprocessing is a vital step that directly affects the performance of AI models. It includes cleaning data, dealing with missing values, normalising data formats, and partitioning data into subsets for training and testing. The integrity of this process ensures the robustness of subsequent model training.
3. Model Development and Training
Once data has been collected and preprocessed, the next step is to build the actual AI model and train it on the collected dataset.
Selecting appropriate algorithms is essential for developing effective AI models. This choice depends on the specific requirements of the project, such as accuracy, processing time, and scalability, and can range from classical methods like decision trees to more advanced state-of-the-art deep learning models.
Training a machine learning model is comparable to guiding a student through a series of examples to grasp underlying patterns and relationships within data. In this phase, the model ingests preprocessed data, and tries to find patterns within the data. For example, if we wanted a model to differentiate between cats and dogs, we would train it on images of cats and dogs. During training, the model would try to understand what differentiates an image of a cat from an image of a dog.
4. Testing and Validation
AI models must be rigorously tested using unseen data sets to evaluate their predictive accuracy and generalisability. This helps identify any overfitting or underfitting issues or biases in the model.
Additionally, it's also important to ensure that the AI model meets all user requirements and performs as expected under varied scenarios. Feedback from stakeholders is crucial here to confirm the model's readiness for deployment.
5. Deployment and Integration
Once built AI models need to be deployed on either cloud or local servers. Often AI models require a lot of compute to run, including the use of GPUs. The latency of the AI model will therefore depend on the hardware its hosted on. So its important to design the server infrastructure keeping in mind the latency requirements and the number of expected requests.
Once deployed, AI models are usually integrated with external applications like web or mobile apps through APIs.
6. Continuous Monitoring & Training
One of the challenges with AI systems is that the 'data distribution' they are trained on continuously changes. For example, overtime a language model will become outdated because the style people use to write language changes, and so does the vocabulary. Therefore, its important to continuously monitor an AI model even after its deployed to determine when re-training is required. This usually happens when the AI model's performance starts decreasing because of a change in the data it is being run on (for example, if you've changed your camera in a computer vision application).
To Sum It Up
The AI Software Development Life Cycle is a specialised adaptation of the traditional software development process, designed to address the unique challenges of AI applications. By following this structured approach, organisations can effectively develop, deploy, and maintain AI systems that are robust, scalable, and capable of driving significant business value.
