Model Framework

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 Model Framework


FrameworkDescriptionCommon Use CasesSupported Formats
TensorFlowOpen-source ML platform by Google. Supports training & deployment.Deep learning, production ML at scale.pb, .h5, SavedModel
KerasHigh-level API for building and training models (now integrated with TF).Rapid prototyping, beginner-friendly.h5, SavedModel
PyTorchFlexible and widely-used for research and prototyping (by Meta).Academic research, dynamic computation.pt, TorchScript, ONNX
ONNXOpen format to represent ML models for interoperability across frameworks.Cross-platform model deployment.onnx
scikit-learnLibrary for classical ML models in Python.Traditional ML (regression, classification)Pickle .pkl, ONNX (via converter)
XGBoostGradient boosting framework optimized for speed/performance.Structured/tabular data tasksBinary .model, JSON, ONNX
TensorRTNVIDIA SDK for high-performance deep learning inference.Model optimization for GPU inferenceSupports ONNX, UFF, TF
Core MLApple’s framework for on-device ML.iOS/macOS apps.mlmodel
TFLiteTensorFlow Lite, optimized for mobile and edge.Mobile, embedded systems.tflite


🔍 Deep Comparison of Model Frameworks


FrameworkPerformanceDeployment CompatibilityBest Use CasesInteroperability
TensorFlowHigh with XLA and GPU support✔ Cloud (GCP, AWS)
✔ Edge (TFLite)
✔ Mobile (Android/iOS)		Enterprise-scale apps, deep learning✔ Supports Keras, TFLite, TensorRT
KerasModerate (depends on backend, usually TF)✔ Cloud (via TF)
✔ Mobile/Edge (via TFLite)		Fast prototyping, smaller projects✔ Fully compatible with TensorFlow
PyTorchHigh for research (less optimized for prod)✔ Cloud (AWS SageMaker, Azure, GCP)
✔ Edge (via PyTorch Mobile)		R&D, NLP, computer vision✔ Exports to ONNX for wider compatibility
ONNXDepends on backend (runtime-agnostic)✔ Edge (ONNX Runtime)
✔ Cloud
✔ Embedded (Raspberry Pi, Jetson)		Cross-framework deployment✔ Converts from TF, PyTorch, scikit-learn
scikit-learnFast for small/mid-size models✔ Cloud
✖️ Not optimized for mobile/edge		Classic ML tasks (classification, regression)✔ Converts to ONNX
XGBoostVery fast with optimized C++ backend✔ Cloud (AWS, GCP, Azure)
✖️ Limited native mobile support		Tabular data, competitions (Kaggle, etc.)✔ ONNX support, limited TFLite/edge support
TensorRTVery high (GPU-accelerated)✔ Edge (Jetson, embedded systems)
✔ Cloud with NVIDIA GPUs		Real-time inference, robotics, autonomous driving✔ Works with TF, ONNX, PyTorch (via ONNX)
Core MLOptimized for Apple silicon (M1/M2)✔ iOS/macOS onlyOn-device iOS apps (AR, vision, voice)✔ Converts from TF, PyTorch, ONNX (via tools)
TFLiteExtremely efficient on mobile/edge✔ Android, iOS, Raspberry Pi, microcontrollersTinyML, mobile AI, offline apps✔ Converts from TF/Keras

🧠 Key Insights

  • Best for Production (Cloud):

    • TensorFlow, PyTorch, ONNX (especially with ONNX Runtime)

  • Best for Mobile/Edge Deployment:

    • TFLite (Android, embedded)

    • Core ML (iOS)

    • TensorRT (NVIDIA Jetson, GPUs)

  • Best for Research & Development:

    • PyTorch (due to dynamic computation)

    • TensorFlow + Keras (for flexibility with production transition)

  • Best for Interoperability:

    • ONNX (acts as a universal translator between frameworks)

  • Model Optimization for Inference:

    • TensorRT > TFLite > ONNX Runtime > Native TF/PyTorch

🛠️ Deployment Tools & Platforms Overview

Deployment TargetRecommended Frameworks & Tools
Cloud (GCP, AWS, Azure)TensorFlow, PyTorch, ONNX
Mobile (iOS)Core ML, TensorFlow Lite
Mobile (Android)TensorFlow Lite, PyTorch Mobile
Edge DevicesTensorRT (NVIDIA), ONNX, TFLite
Browser/WebTensorFlow.js, ONNX.js
Embedded/IoT (e.g. microcontrollers)TensorFlow Lite Micro





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