RAG vs Fine-Tuning: Choosing the Right LLM Approach for Your Product
Both RAG and fine-tuning improve LLM performance on your specific use case — but they solve different problems. Here's how to choose.
MLOps
MLflow
ML experiment tracking, model registry, and production serving with MLflow
10M+Monthly downloads
20+ML frameworks supported
ModelRegistry + versioning
OpenApache 2.0 licensed
HOW WE USE IT
MLflow in our stack
We implement MLflow as the central tracking and model management layer in production ML platforms. Experiment tracking, model versioning, the model registry, and MLflow's serving capabilities make it the backbone of our MLOps implementations.
CAPABILITIES
What we deliver
- MLflow tracking server setup and management
- Experiment and run logging with autologging
- Model registry with staging/production lifecycle
- MLflow Models for multi-framework serving
- Integration with SageMaker, Vertex AI, and Azure ML
- Custom model flavors and preprocessors
USE CASES
How we apply MLflow
ML Platform Foundation
MLflow as the experiment tracking and model registry for a team of 5+ data scientists — consistent logging from day one.
Model Deployment Pipeline
MLflow + Kubernetes: registered models automatically deployed to staging/production via GitOps trigger.
A/B Model Testing
Model registry with champion/challenger staging for safe online model experimentation with traffic splitting.
Engineering Stack
Built with the tools that matter
38 production-grade technologies — every one battle-tested in shipped products.
Frequently Asked Questions
Didn't find what you were searching for? Reach out to us at [email protected] and we'll assist you promptly.
MLflow is open-source, self-hostable, and integrates with every major ML framework (PyTorch, TensorFlow, scikit-learn, XGBoost, HuggingFace). Its model registry provides a vendor-neutral staging and promotion workflow that integrates with SageMaker, Vertex AI, and Azure ML for deployment. We choose MLflow when control, portability, and cost matter — or when integrating with cloud ML platforms that have native MLflow support. Weights and Biases has better visualization and collaboration features for research teams focused on experiment exploration.
A production MLflow deployment includes: a centralized tracking server with a PostgreSQL backend and S3/GCS artifact store, experiment organization by model type and version, autologging for all training runs, the model registry with staging to production promotion gates, webhook integrations to trigger downstream deployment pipelines, and team-level access control. We integrate MLflow tracking into training scripts as the first step — so nothing is lost from experiment to production.
Setting up a production MLflow tracking server with a model registry and CI/CD integration typically takes 2-4 weeks. Retrofitting MLflow tracking into an existing ML codebase (adding autologging, organizing experiments, and setting up the registry) takes 1-3 weeks. A full MLOps platform with MLflow at the center — including training pipelines, automated evaluation, and deployment triggers — is part of a larger 8-12 week infrastructure project.
FROM OUR CLIENTS
Built with teams who ship
The team took our AI concept from whiteboard to production in 10 weeks. The architecture they designed handles 10x our expected load with no issues.
Insights
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