MLOps for LLM: A Case Study on Dresscode
Moving from a proof of concept to a real product is hard.
I built Dresscode, an AI stylist. It uses Gemma 4 to digitize wardrobes and suggest outfits based on real-time weather.
A great idea needs more than just a model. It needs MLOps.
MLOps keeps your AI accurate, reliable, and cheap to run. Here is the 7-step pipeline I use to scale AI.
Data Ingestion and Engineering Raw data is messy. For Dresscode, users upload high-res photos. • Ingestion: We move photos to cloud storage via API. • Engineering: We compress 12MB smartphone photos to save costs and speed up processing. We also strip metadata for privacy. • Text Cleaning: We clean weather API data to keep prompts short and efficient.
Feature Store Features are the specific details an AI uses to make decisions. • For images: We store mathematical embeddings (vectors). This prevents us from re-processing the same image twice. • For weather: We convert raw data into categories like "chilly" or "rainy." • The Benefit: A Feature Store lets you pull these details instantly instead of recalculating them.
Model Training and Experimentation We do not train Gemma 4 from scratch. We focus on Prompt Engineering and evaluation. • Experimentation: We test different system prompts to ensure the AI outputs clean JSON. • CI (Continuous Integration): We use a "Golden Dataset" of 100 photos. Every time we change a prompt, the system checks if accuracy stays above 95%.
Model Registry Think of this as an app store for your models. • We store versioned prompts and model configurations. • If a new prompt causes the AI to recommend a coat in summer, we can click "Rollback" to go to a stable version instantly.
Continuous Deployment and Serving This is how you get the model to the user. • Visual Tasks: We use asynchronous queues. Users upload photos, and we process them in the background so the app stays fast. • Text Tasks: We use token streaming. This shows the outfit suggestion word-by-word so the user is not staring at a loading screen.
Continuous Monitoring AI can degrade over time. We monitor three things: • System Performance: Is the latency increasing? • Data Drift: Are users uploading new photo formats we did not expect? • Model Accuracy: Is the AI starting to hallucinate items the user does not own?
The Feedback Loop The system must learn from mistakes. We capture user corrections and feed that data back into step one to retrain and improve the model.
MLOps turns a cool demo into a professional tool.
Source: https://dev.to/saad4software/mlops-for-llm-a-case-study-on-dresscode-3joj
Optional learning community: https://t.me/GyaanSetuAi