Real-Time Model Inference with Apache Kafka and Flink for Predictive AI and GenAI

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 a 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.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch.

Artificial Intelligence (AI) and Machine Learning (ML)

Artificial Intelligence (AI) and Machine Learning (ML) are pivotal in transforming how businesses operate by enabling systems to learn from data and make informed decisions. AI is a broad field that includes various technologies aimed at mimicking human intelligence, while ML is a subset focused on developing algorithms that allow systems to learn from data and improve over time without being explicitly programmed. The major use cases are predictive AI and generative AI.

AI/ML = Model Training, Model Deployment and Model Inference

In AI/ML workflows, model training, model deployment and model inference are distinct yet interconnected processes:

• Model Training: Using historical data or credible synthetic to build a model that can recognize patterns and make predictions. It involves selecting the right algorithm, tuning parameters, and validating the model’s performance. Model training is typically resource intensive and performed in a long-running batch…