MirahLabs Engineering Blog
Technical insights, tutorials, and architectures written by our design and backend engineers.
MLOps: Building Reproducible ML Pipelines with MLflow and DVC
Machine learning without MLOps produces science experiments, not production systems. Learn how MLflow tracks experiments and DVC versions datasets to build reproducible, deployable ML pipelines.
Understanding Transformer Architecture: Attention Is All You Need
A deep-dive into the Transformer model that revolutionized natural language processing—from self-attention heads to positional encoding and multi-head parallelism.
Prompt Injection Vulnerabilities in LLM Applications and How to Prevent Them
Prompt injection allows malicious actors to hijack LLM behavior. Learn how to protect your applications from direct and indirect prompt injection attacks.
Understanding LLM Hallucinations: Causes, Detection, and Prevention
LLM hallucinations—confidently wrong answers—are the most critical reliability challenge in production AI. Learn why they happen, how to detect them, and architectural strategies to minimize them.
Deploying ML Models to Production: FastAPI + Docker + Kubernetes
Getting an ML model from Jupyter notebook to production requires API serving, containerization, and orchestration. This end-to-end guide covers model serving with FastAPI, containerization, and Kubernetes deployment.
AI Agents and Tool Use: Building Autonomous Workflows with LangGraph
LangGraph enables stateful, multi-step AI agent workflows with cyclic graphs. Learn how to build reliable autonomous agents that use tools, handle errors, and maintain state.