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.
AI / ML
TensorFlow
TensorFlow Lite for edge AI and TFX for production ML pipelines
170K+GitHub stars
<1MBTFLite mobile models
TPUGoogle hardware native
20+ML pipeline stages TFX
HOW WE USE IT
TensorFlow in our stack
We use TensorFlow for production ML pipelines (TFX), mobile AI (TensorFlow Lite), and Google Cloud ML deployments. TF is our choice when edge deployment on Android, embedded systems, or browser-based inference (TF.js) is required.
CAPABILITIES
What we deliver
- TensorFlow Lite for Android and embedded devices
- TFX production ML pipelines with Apache Beam
- TF.js for in-browser model inference
- Keras model development and training
- Model quantization for mobile deployment
- TensorBoard visualization and monitoring
USE CASES
How we apply TensorFlow
Android ML Feature
TFLite model for real-time on-device inference in an Android app — zero network latency, full offline capability.
IoT Edge Inference
Quantized TFLite model running on Raspberry Pi / edge hardware for real-time sensor data classification.
Browser AI
TF.js model running entirely in the browser for client-side ML without sending data to a server.
Engineering Stack
Built with the tools that matter
38 production-grade technologies — every one battle-tested in shipped products.
Frequently Asked Questions
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TensorFlow has specific advantages: TFLite is the most mature mobile and edge deployment framework for on-device inference; TensorFlow.js enables browser and Node.js inference; TF Serving is battle-tested for high-throughput model serving; and TF Extended (TFX) provides an end-to-end ML pipeline framework. We recommend TensorFlow when you need TFLite for mobile deployment, TF.js for browser inference, or when the team has an existing TF2/Keras codebase and switching to PyTorch is not justified.
A production TensorFlow deployment includes: SavedModel format for serving, TF Serving for model endpoint management, TFLite conversion for mobile/edge deployment, quantization for size and latency optimization, TFX pipeline for automated retraining and evaluation, and monitoring with TensorBoard or integrated ML observability tools. For edge deployments, we optimize models to run within memory and compute constraints of target hardware.
Converting an existing TensorFlow model to a production serving setup takes 2-4 weeks. A new TFLite edge deployment project — model training, optimization, and device integration — takes 6-10 weeks. A full TFX pipeline with automated retraining takes 8-12 weeks.
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.
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