This discussion primarily focuses on cloud-native and distributed systems-related technology stacks. These stacks are expected to undergo significant transformations in 2024 due to the rapid advancement of AI, leading to either a renaissance of older technologies or a surge in newer ones. They will play a crucial role in cost reduction and efficiency improvement for AI applications.

  1. Kubernetes: The cornerstone of cloud-native technology, essential for orchestrating containerized applications and services. Its role in scalable AI applications is unmatched, with its importance only set to grow alongside increasing data and computational demands.

  2. TensorFlow/Keras/PyTorch: Fundamental AI/ML frameworks, evolving to become more user-friendly and versatile. They stand out for their growing community support and continuous introduction of advanced features for AI and ML development.

  3. Edge Computing: Plays a critical role in reducing latency and enabling real-time data processing in distributed AI systems. It’s particularly crucial for AI applications in IoT and efficient processing of data from numerous devices.

  4. FAAS (Function as a Service) Platforms: Increasingly important for scalable, event-driven computing solutions, these platforms are crucial in handling dynamic workloads in AI applications.

  5. WebAssembly: Enhances distributed systems’ performance and portability by enabling AI models to run on the client side, reducing server load.

  6. Vector Database (e.g., Croma, Milvus, Pinecone, Faiss, Weaviate): These are becoming increasingly important for complex AI-driven queries and data-intensive applications.

  7. GraphQL: Essential for creating efficient APIs, playing a significant role in data management strategies within AI ecosystems.

  8. Istio: Its role in microservices architecture, especially in service mesh, is becoming increasingly critical in AI applications for service discovery, load balancing, and fault handling.

  9. Redis: Indispensable in AI applications, both as a vector database and a rapid storage medium for caching and feature storage.

  10. Kafka: A vital component for real-time data streaming and integration in AI systems, serving as an “energy pipeline” for data acquisition.

  11. Serverless Computing: Extends the capabilities of FAAS, allowing for the building of more agile, cost-effective applications. It’s particularly beneficial for AI-based applications with variable workload patterns.

  12. AI Model Deployment Platforms (e.g., NVIDIA Triton Inference Server, TensorFlow Serving): These platforms are gaining importance for efficiently deploying and managing AI models in production environments, facilitating smoother transitions from development to operational stages.