Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized the way we approach problem-solving and data analysis. These technologies are powering a wide range of applications, from recommendation systems and autonomous vehicles to healthcare diagnostics and fraud detection. However, deploying and managing ML models in production environments can be a daunting task. This is where containerization comes into play, offering an efficient solution for packaging and deploying ML models.
In this article, we’ll explore the challenges of deploying ML models, the fundamentals of containerization, and the benefits of using containers for AI and ML applications.