AI development is the process by which companies create and operationalize artificial intelligence systems. It involves the entire AI lifecycle from problem definition and data collection through Machine Learning model selection, architecture design, validation and testing, iterative refinement and deployment.
Understanding your business challenge, selecting the right models and ensuring ethical considerations are integrated throughout the process is key. AI can be used for a variety of tasks, including natural language processing (NLP), computer vision, decision making, and even in robotics to perform physical actions. However, AI is only as effective as the underlying infrastructure that supports it. Many AI initiatives stall well before production due to missteps that occur in the early stages of planning and design.
Jumping directly to model selection without first clearly defining the business problem: This can lead to feature creep and misaligned expectations. Assuming that data is ready for AI: Unstructured, incomplete, or skewed data can derail even the best-performing models. Data must be curated, structured and labeled before AI can function reliably.
Ignoring the importance of evaluation: Shipping a feature does not mean that it is ready for production. Teams that skip evaluation can run into issues like latency, accuracy, and output quality. AI must be validated against real-world inputs and in edge cases. Additionally, ignoring feedback mechanisms can result in poor performance or even outright failure. Implementing telemetry, rating systems and prompt adjustment workflows allows teams to refine or replace models that fail in the field.
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