Artificial intelligence (AI) and machine Learning (ML) are transforming business operations by enabling systems to learn from data and make intelligent decisions for predictive and generative AI use cases. Two essential components of AI/ML are model training and inference. Models are developed and refined using historical data. Model inference is the process of using trained machine learning models to make predictions or generate outputs based on new, unseen data.
This blog post covers the basics of model inference, comparing different approaches like remote and embedded inference. It also explores how data streaming with Apache Kafka and Flink enhances the performance and reliability of these predictions. Whether for real-time fraud detection, smart customer service applications, or predictive maintenance, understanding the value of data streaming for model inference is crucial for leveraging AI/ML effectively.