Introduction
Building deep learning models is only half the journey. Deployment ensures models can serve predictions in real-world applications. TensorFlow Serving is an open-source, flexible, high-performance system specifically designed to deploy ML models at scale.
At CuriosityTech.in, learners in Nagpur gain hands-on experience serving models for web apps, mobile apps, and enterprise pipelines, ensuring they are career-ready for production-level AI roles.
1. What is TensorFlow Serving?
TensorFlow Serving is a production-ready framework to deploy models efficiently.
Key Features:
- Supports TensorFlow models and custom models
- Provides REST and gRPC APIs for serving predictions
- Enables version control for model updates
- Optimized for low latency and high throughput
Analogy: Think of TensorFlow Serving as a restaurant kitchen. The chef (model) prepares dishes (predictions), and the waiter (API) delivers them to customers (applications) seamlessly.
2. Why Deploy Models?
- Real-time Predictions: Power apps like chatbots, recommendation systems, or autonomous systems
- Scalable Architecture: Serve multiple users simultaneously
- Version Management: Update models without downtime
- Integration: Connect with web servers, mobile apps, or cloud platforms
CuriosityTech Insight: Students learn that deployment skills make them stand out for AI engineering roles, as production-ready models are highly valued in the industry.
3. Step-by-Step Guide to Deploy a Model
Step 1 – Export the Trained Model
model.save(“saved_model/my_cnn_model”)
- Saves the model in TensorFlow’s SavedModel format, including architecture, weights, and metadata
Step 2 – Install TensorFlow Serving
# Using Docker
docker pull tensorflow/serving
Step 3 – Serve the Model
docker run -p 8501:8501 –name=tf_serving_cnn \
–mount type=bind,source=$(pwd)/saved_model/my_cnn_model,target=/models/my_cnn_model \
-e MODEL_NAME=my_cnn_model -t tensorflow/serving
Observation: Learners see the model ready to serve predictions via REST API on port 8501.
Step 4 – Send Requests
import requests
import json
import numpy as np
data = json.dumps({“signature_name”: “serving_default”, “instances”: np.random.rand(1,32,32,3).tolist()})
response = requests.post(‘http://localhost:8501/v1/models/my_cnn_model:predict’, data=data)
print(response.json())
- Model predicts in real-time for the provided input
4. Model Versioning
- TensorFlow Serving allows multiple versions of a model simultaneously
- Enables rolling updates without downtime
- Useful for A/B testing and production validation
CuriosityTech Example: Students deploy two versions of a CNN classifier, testing one on live data while keeping the previous version as fallback.
5. Integration with Applications
- Web Applications: Connect via REST API to Flask, Django, or Node.js
- Mobile Apps: TensorFlow Lite can be used with Serving for backend predictions
- Enterprise Pipelines: Integrate with Kubernetes or cloud platforms for scalable deployments
Enterprise Use Case:
A student deployed a defect detection CNN model for a factory. The model predicted defects in real-time via REST API, reducing manual inspection errors by over 50%, demonstrating industry-level impact.
6. Performance Optimization
- Use GPU acceleration for high-throughput inference
- Enable batching to serve multiple requests efficiently
- Monitor latency and throughput metrics to optimize production models
Career Insight: AI engineers with deployment expertise are in high demand for companies focusing on real-time AI applications, autonomous systems, and enterprise AI solutions.
7. Human Story
A learner at CuriosityTech successfully deployed a traffic sign classifier for a simulation project. Initially, the REST API had high latency, but after GPU acceleration and request batching, the model served predictions within milliseconds. This hands-on experience emphasized the importance of optimization and real-world deployment skills.
Conclusion
Deploying deep learning models with TensorFlow Serving bridges the gap between model development and production-ready applications. At CuriosityTech.in, learners gain hands-on experience in serving, integrating, versioning, and optimizing models, ensuring they are prepared for real-world AI engineering roles and scalable enterprise solutions.
