TensorFlow

For most new ML projects, PyTorch is the pragmatic default in 2026 — it has 85% research share, 55% production share, leads in AI job postings (37.7% vs 32.9%), and the Hugging Face ecosystem defaults to it. Choose TensorFlow when: you need mobile deployment via TFLite (more mature than PyTorch Mobile), browser AI via TF.js (no PyTorch equivalent), a production MLOps pipeline via TFX, Google Cloud TPU training, or you're maintaining existing TensorFlow infrastructure. Keras 3 bridges the gap by running on both backends.

TensorFlow Lite is TensorFlow's framework for on-device inference — mobile phones (Android/iOS), edge devices (Raspberry Pi, Arduino), and microcontrollers (TFLite Micro). Use TFLite when: model inference must work offline, data cannot leave the device (privacy), server inference latency is unacceptable for the use case, or you need sub-10ms real-time inference on mobile hardware. TFLite models run 4× smaller (INT8 quantization) than standard TF models with GPU and NPU hardware delegate acceleration.

Keras 3 is a multi-backend deep learning API that runs on TensorFlow, JAX, and PyTorch. You write model architecture in Keras; the backend is a configuration choice (KERAS_BACKEND=tensorflow/jax/torch). This means: train on JAX for TPU speed, convert to PyTorch to access Hugging Face fine-tuning tools, deploy to TFLite via the TensorFlow backend — all from the same model code. Keras 3 significantly reduces framework lock-in and is the recommended way to write new TensorFlow/JAX models.

TensorFlow Extended (TFX) is an ML production pipeline framework: data ingestion, validation, preprocessing, training, evaluation, and deployment as composable, reusable pipeline components. TFX prevents data quality issues from reaching production, automates model retraining, enforces model quality gates before deployment, and provides reproducible ML pipelines. Need TFX when: models need regular retraining on new data, data quality issues could silently degrade model performance, or you need regulatory documentation of model training decisions. For one-off models with infrequent updates, TFX's setup cost outweighs benefits.

TensorFlow itself is free and open-source. Training infrastructure costs depend on model size and compute time: GPU instance costs for training (typically $1-5/hour for mid-size models), cloud training via Vertex AI Training, or TPU v5 for large model training (often 3-5× more cost-efficient than GPUs). Development effort depends on model complexity, data preparation, TFX pipeline requirements, and deployment targets. Share your ML use case and we'll provide a scoped development and infrastructure estimate.

TensorFlow Serving for server-side deployment: Docker container, versioned model directories, REST and gRPC API endpoints, A/B model routing, and Kubernetes HPA autoscaling. TFLite for mobile: converter runs INT8 quantization, Android integrates via ML Kit or TFLite interpreter, iOS via CoreML bridge or TFLite interpreter. TF.js for browser: model conversion via tensorflowjs_converter, loading in the browser with WebGL GPU acceleration. We recommend TFX to automate the training → validation → deployment transition for models that retrain regularly.

Transfer learning uses a model pre-trained on large datasets (ImageNet for vision, BERT for NLP) as a starting point, fine-tuning it on your domain data. In TensorFlow: load from TensorFlow Hub, freeze pre-trained layers, add task-specific output layers, train on your data. Result: production-quality models from 1,000–10,000 examples in hours vs training from scratch requiring millions of examples and weeks. Use transfer learning for: any computer vision task (EfficientNet, MobileNet), text classification (BERT, DistilBERT), and sentence embeddings (USE). Train from scratch only for truly novel architectures or unique data distributions.

TFLite model optimization process: (1) post-training quantization — convert float32 to INT8, reducing size 4× and enabling NPU acceleration; (2) representative dataset calibration for accurate INT8 quantization; (3) hardware delegate selection (GPU, NNAPI, CoreML, Hexagon DSP) matching the target device; (4) model architecture selection — MobileNet and EfficientNet-Lite are designed for mobile constraints; (5) benchmark on target hardware with TFLite Benchmark Tool. Goal: models under 10MB, inference under 10ms on mid-range devices.

Use Vertex AI AutoML when: the task is standard (image classification, object detection, text classification, tabular prediction), you don't need custom architectures, and fast time-to-production matters more than model control. Use TensorFlow when: custom architectures are required, you need TFLite mobile deployment (AutoML exports to TFLite but with less control), you need TF.js browser inference, you need full TFX pipeline control, or model training cost at scale makes managed compute prohibitive. Many teams start with AutoML to validate feasibility, then migrate to TensorFlow for custom optimization.

We provide TensorFlow managed support covering TensorFlow and TFX version upgrades, model performance monitoring and drift detection, TFLite model optimization for new target devices (as hardware NPUs evolve), TFX pipeline maintenance for data schema changes, and TensorFlow Serving infrastructure tuning for changing inference patterns. We also conduct model accuracy audits and retraining data analysis for models that degrade in production.