Putting AI models into production and making them available for real-world use, including techniques for improving accuracy, reliability, and integration with existing systems.
AI deployment encompasses everything that happens after a model is trained or selected. It’s the process of taking an AI model and making it available to end users in a reliable, scalable, and secure way.
Deployment involves:
| Term | Description |
|---|---|
| Batch Processing | Grouping multiple inference requests together for simultaneous processing, maximizing GPU utilization and throughput. |
| Beam Search | A heuristic search algorithm used in sequence generation that explores multiple possible sequences simultaneously, keeping only the top ‘k’ most probable candidates. |
| Caching | Storing and reusing previous computation results to avoid redundant work, reducing latency and costs. |
| Edge Computing | Running AI inference on local devices rather than cloud infrastructure, enabling low-latency, privacy-preserving applications. |
| Feature Store | A centralized infrastructure layer that manages, stores, and serves ML features for both training and inference, ensuring consistency and enabling reuse. |
| Graphics Processing Unit (GPU) | Specialized hardware with thousands of parallel cores, foundational for training and running AI models due to its ability to perform massive parallel computations. |
| Grounding | The process of anchoring an AI model’s output to specific, verifiable external sources to ensure factual accuracy. |
| Inference | The phase where a trained model is used to make predictions on new data, optimized for speed and scale. |
| Inference-Time Compute | Computational resources used during inference, with reasoning models using significantly more to achieve better performance. |
| KV Cache (Key-Value Cache) | An inference optimization that stores Key and Value tensors from previous tokens, avoiding redundant recomputation during autoregressive generation. |
| Latency | The time delay between a request and response, critical for user experience and system responsiveness. |
| MCP (Model Context Protocol) | An open standard protocol for universally connecting LLMs to external data sources, tools, and systems. |
| Model Serving | The infrastructure and software systems that deploy trained models into production, making them accessible via APIs for real-time predictions. |
| Observability | The ability to understand AI system behavior in production through metrics, logs, and traces. |
| Orchestration | The coordination of multiple AI components, models, tools, and data sources to execute complex workflows. |
| Prompt Injection | A security attack where malicious input manipulates an AI system’s behavior, causing it to ignore instructions or perform unintended actions. |
| Quantization | A technique that reduces the precision of model weights (e.g., from 16-bit to 4-bit) to decrease memory usage and speed up inference. |
| RAG (Retrieval-Augmented Generation) | An AI framework that improves LLM accuracy by grounding responses in external, up-to-date, or proprietary data sources, reducing hallucinations. |
| Sampling | The process of selecting the next token from a model’s probability distribution, controlling the trade-off between determinism and diversity. |
| Semantic Search | A search methodology that retrieves results based on meaning and intent rather than exact keyword matches, using vector embeddings. |
| Speculative Decoding | An inference optimization using a small draft model to generate candidate tokens, verified in parallel by a larger target model for 2-3x speedup. |
| Streaming | A response delivery method where AI outputs are transmitted token-by-token as generated, reducing perceived latency and enabling interactive experiences. |
| Temperature | A parameter controlling the randomness and creativity of a language model’s output. |
| Throughput | The number of requests or tokens an AI system can process per unit of time, measuring capacity and scalability. |
| Top-p Sampling (Nucleus Sampling) | A decoding strategy that dynamically filters the vocabulary to the smallest set of tokens whose cumulative probability exceeds a threshold $p$, balancing creativity and coherence. |
| Vector Database | A specialized database for storing and searching high-dimensional embeddings, enabling fast semantic search. |
A brilliant AI model is useless if it can’t be deployed effectively. Deployment considerations include:
Understanding deployment helps organizations move from AI experiments to production-ready solutions that deliver real business value.