Master MLOps practices for production machine learning systems. Learn data versioning, experiment tracking with MLflow, CI/CD for ML, model registry governance, and monitoring strategies for AWS, Azure, and GCP.
Read more →Category: Technology Engineering
Technology Engineering
LangGraph Unleashed: Building Stateful Multi-Agent AI Systems with Graph-Based Workflows
Introduction: LangGraph represents a paradigm shift in how we build AI agents. While LangChain excels at linear chains and simple agent loops, LangGraph introduces a graph-based approach that enables complex, stateful, multi-actor applications with cycles, branching, and human-in-the-loop interactions. Released by LangChain Inc. in early 2024, LangGraph has quickly become the go-to framework for building […]
Read more →Tool Use and Function Calling: Extending LLM Capabilities with External Actions
Introduction: Function calling transforms LLMs from text generators into action-taking agents. Instead of just producing text responses, models can now decide when to call external functions, APIs, or tools to accomplish tasks. This capability enables building assistants that can search the web, query databases, send emails, execute code, and interact with any system that exposes […]
Read more →Structured Output from LLMs: Instructor Library and Production Patterns (Part 2 of 2)
Introduction: Getting LLMs to return structured data instead of free-form text is essential for building reliable applications. Whether you need JSON for API responses, typed objects for downstream processing, or specific formats for data extraction, structured output techniques ensure consistency and parseability. This guide covers the major approaches: JSON mode, function calling, the Instructor library, […]
Read more →LLM Deployment Strategies: From Model Optimization to Production Scaling
Introduction: Deploying LLMs to production is fundamentally different from deploying traditional ML models. The models are massive, inference is computationally expensive, and latency requirements are stringent. This guide covers the strategies that make LLM deployment practical: model optimization techniques like quantization and pruning, inference serving with batching and caching, containerization with GPU support, auto-scaling based […]
Read more →LLM Fine-Tuning Techniques: From LoRA to Full Parameter Training
Introduction: Fine-tuning transforms general-purpose LLMs into specialized models that excel at your specific tasks. While prompting can get you far, fine-tuning unlocks capabilities that prompting alone cannot achieve: consistent output formats, domain-specific knowledge, reduced latency from shorter prompts, and behavior that would require extensive few-shot examples. This guide covers the practical aspects of LLM fine-tuning: […]
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