Explore the integration of AI and Machine Learning in Event-Driven Architectures to enhance event processing, predictive analytics, and automated decision-making.
Explore the fundamental concepts of Artificial Intelligence and Machine Learning, their differences, applications, and integration into software systems.
Explore the diverse types of AI systems, their characteristics, and applications. Understand the differences between narrow and general AI, and learn how to choose the right AI techniques for your projects.
Explore the intricate patterns of feature engineering and transformation to enhance AI model accuracy. Learn about normalization, encoding, scaling, and automation techniques with practical examples in JavaScript and TypeScript.
Explore the Model Training Pipeline Pattern in AI, focusing on automation, scalability, and best practices for efficient model training and deployment.
Explore comprehensive strategies and best practices for deploying AI models, including real-time serving, edge deployment, and using tools like TensorFlow Serving. Learn about containerization, scaling, versioning, and MLOps for efficient AI model deployment.
Explore the techniques and principles of privacy-preserving machine learning, including federated learning, differential privacy, and homomorphic encryption, to protect individual data privacy in AI models.
Explore how to incorporate AI services into microservices architectures, focusing on AI use cases, frameworks, scalable models, independent deployment, data privacy, integration with data pipelines, and model maintenance.
Explore design patterns for integrating machine learning into Java applications, including data pipelines, model serving, and feature stores, with practical examples and best practices.