TensorFlow
What it is: End-to-end open source deep learning platform from Google designed for production deployment at scale.
What It Does Best
Production deployment. TensorFlow Serving for APIs, TF Lite for mobile, TF.js for web. Complete deployment ecosystem built-in. No additional tools needed.
Enterprise scalability. Runs on CPUs, GPUs, TPUs, mobile, edge devices, browsers. One framework, deploy anywhere. Proven at Google scale.
Complete ML platform. Data loading (tf.data), training, deployment, monitoring in unified ecosystem. Not just a framework - entire production pipeline.
Key Features
Keras integration: High-level API built into TensorFlow 2.x
TF Serving: Production model serving with REST/gRPC APIs
TF Lite: Deploy to mobile (iOS, Android) and embedded devices
TF.js: Run models in browsers and Node.js
TensorBoard: Visualization for training and model inspection
Pricing
Free: Open source (Apache 2.0 license)
Commercial: No licensing costs for any use
Cloud: Free software, Google Cloud TPU pricing varies
When to Use It
✅ Deploying models to production at scale
✅ Mobile or web ML applications
✅ Using Google Cloud or TPUs
✅ Need complete deployment ecosystem
✅ Enterprise production requirements
When NOT to Use It
❌ Research and rapid prototyping (PyTorch more flexible)
❌ Learning deep learning (PyTorch easier to start)
❌ Classical ML only (scikit-learn simpler)
❌ Small projects (complexity overkill)
❌ Need dynamic computation graphs (PyTorch better)
Common Use Cases
Production APIs: Scalable model serving with TF Serving
Mobile apps: On-device ML for iOS and Android
Web applications: In-browser ML with TensorFlow.js
Large-scale training: Distributed training on TPUs
Computer vision: Image classification, object detection
TensorFlow vs Alternatives
vs PyTorch: TensorFlow better deployment, PyTorch better research
vs Keras: Keras is now the official high-level API of TensorFlow
vs ONNX: TensorFlow full framework, ONNX for interoperability
Unique Strengths
Production ecosystem: Most complete deployment tooling
Google backing: Powers Google Search, Gmail, Photos
Multi-platform: Cloud, mobile, web, edge - deploy anywhere
TPU support: Best integration with Google's custom chips
Bottom line: Best framework for production ML deployment at scale. Choose TensorFlow when your model needs to serve millions of requests, run on mobile devices, or integrate with Google Cloud. More complex than PyTorch but unmatched deployment ecosystem.