“mlops”
MLOps for LLMs: Versioning Prompts, Data, and Weights
LLM apps drift for reasons unrelated to weights. Version prompts, datasets, and models against the metrics that decide whether a change is an improvement, using MLflow tracking plus the Model Registry.
The Art of the Prompt: How to A/B Test Your Prompts in a Live Application
Discover how to systematically improve your LLM applications by implementing A/B testing for prompts in a live environment.
Containerize It: A Guide to Deploying Your AI App with Docker and FastAPI
Learn how to package your AI applications into portable, scalable containers using Docker and the high-performance FastAPI framework.
More Than a Hub: A Developers Guide to the Hugging Face Ecosystem
A deep dive into why Hugging Face is the core of modern AI development, exploring the Hub, Transformers, and the broader ecosystem.
The Missing Piece: How to Monitor and Log Your LLM Apps for Cost and Performance
Building an LLM app is only the first step. Learn how to track tokens, costs, and response quality to ensure your application stays efficient and reliable.
From Notebook to Production: A Developer's First Look at MLOps for AI
Learn the fundamentals of MLOps for AI applications, moving from Jupyter Notebooks to robust, production-ready deployments.
Serverless AI is Here: Deploying Language Models with AWS Lambda
Move your AI application from a script to a scalable, production-ready serverless API using AWS Lambda, API Gateway, and Docker.