The “Life Cycle” in AI refers to the sequential stages an AI project goes through, from initial conception to deployment and maintenance. This cycle usually includes stages such as problem definition, data collection, model development, validation, deployment, and monitoring.
Imagine you’re making a journey from your home to a funfair. The way you prepare for this journey, how you choose your route, how you ensure your safety during the journey, how you keep track of your route so you won’t get lost, and how you ensure you make it back home safely – this entire journey is similar to the AI ‘Life Cycle’. It’s a step-by-step journey to create an AI model and put it out in the world so it can solve problems.
The “Life Cycle” in the context of AI is the process that oversees the development and deployment of an artificial intelligence or machine learning project. The life cycle comprises of six main steps:
Problem Definition: Here, the team identifies the general problem that the AI should solve. Decisions are made concerning the ways in which the AI can help solve the identified problem.
Data Collection and Pre-processing: The AI team collects relevant data that will be used to train and validate the AI model. This stage also involves cleaning the gathered data to ensure it is of good quality for model training.
Modeling: The team selects an appropriate model based on the problem definition and the available data. The data is then used to train the model.
Validation and Tuning: After training, the resultant model is tested using a separate set of data (validation data). This allows the team to fine-tune and optimize the model to balance bias and variance and ensure it generalizes well to unseen data.
Deployment: The AI model, once validated and tuned, is then deployed into a real-world environment where it can start making predictions or classifications based on new data it hasn’t seen before.
Monitoring and Maintenance: After deployment, ongoing monitoring is performed to ensure that the model keeps performing as expected. The AI model might require periodic retraining with new data to maintain its performance.
Each of these stages is critical and skipping one can lead to an ineffective AI system. They each present unique challenges and require the combined efforts of data scientists, engineers and domain experts.
Problem Definition, Data Collection, Data Pre-processing, Modeling, Validation, Tuning, Deployment, Monitoring, Maintenance