Many engineers who are looking for an ML provider for their mobile project consider Amazon SageMaker. It’s a platform to build, train, and deploy machine learning models and is a part of the Amazon Web Services family. The platform includes a selection of tools that span the machine learning project lifecycle, from managing datasets, all the way deploying and managing models in production. Though it does have a toolkit (SageMaker Neo) for deploying models to edge devices like mobile phones, SageMaker is primarily focused on cloud-based deployment, making some of its features limited in terms of helping build and deploy mobile-ready models.
Fritz AI Studio is a machine learning platform built specifically for iOS and Android developers who may not have an ML background. Customers use it to generate and collect labeled datasets, train optimized models without code, deploy and manage on any mobile platforms, and improve models and app UX based on real-world data.
This free guide compares six different ML providers: Amazon SageMaker, Firebase ML, TensorFlow Lite, Create ML, Clarafai, and Fritz AI. See how each provider compares across data management, model training and management, deployment, model iteration, and more.
Download NowDATA | FRITZ AI | AMAZON SAGEMAKER |
Data Generation | ✅ Labeled synthetic data generation | ❌ Data generation unavailable |
Data Upload | ✅ COCO dataset bulk upload; zip file upload; individual image upload | ✅ Bulk data upload and management |
Data Labeling | ✅ In-app labeler:
| ✅ Access to labelers through Amazon Mechanical Turk |
Data Export | ✅ Export labeled dataset snapshots | ✅ Export via Jupyter Notebook + RESTful API endpoint |
MODEL TRAINING | ||
Model Training Interface | ✅ No-code | ✅ Code via Jupyter Notebooks ✅ No-code via SageMaker Autopilot |
Advanced Configurations | ✅ Fine-tune model inputs and outputs, configure performance requirements, and more | ✅ Automatic model training |
Mobile-Specific Optimizations | ✅ Use-case-based model variants; mobile-optimized model architectures | ✅ SageMaker Neo allows models to be trained once and run on the edge |
MODEL MANAGEMENT | ||
Model Export Types Supported | ✅ Core ML (iOS); TensorFlow Lite (Cross-platform): SnapML (Snapchat) | ✅Core ML (iOS) and TensorFlow Lite (Cross-platform), with conversion via CLI |
Model Versioning | ✅ Keras/Model checkpoints; | ✅ Model checkpoints |
Model Protection/Encryption | ✅ Model encryption available | ❌ Model encryption unavailable |
MODEL DEPLOYMENT | ||
Model Pre- and Post-Processing | ✅ Built-in processing with Fritz SDK | ❌ Manual pre- and post-processing |
Platforms Supported | ✅ iOS, Android, Snapchat | ✅ iOS, Android |
MODEL RETRAINING & ITERATION | ||
Ground-truth Data Collection | ✅ Collect and label real-world data, and retrain models on it | ✅ Build and manage accurate training sets using Amazon SageMaker Ground Truth |
Model Iteration | ✅ Model Checkpoints | ✅ Debug & profile training runs ✅ Automatic iteration management |
Model Updates | ✅ Over-the-air model downloads | ❌ Manual model updates required |
PRICING & PLANS | ||
Free Tier | ✅ Permanent with monthly global usage limits; 14-day free trials for all paid subscription plans | ✅ Free for two months with usage limits |
Pricing Structure | ✅ Subscription-based pricing | ✅ Usage-based, per training hour |
SUPPORT | ||
Available Support | ✅ Plan-based support includes: Community forum, Slack, email, phone, and dedicated success manager | ✅ Ticket-based support |
This free guide breaks down the top mobile ML technologies and reviews them based on key components:
This guide offers product specs for Amazon SageMaker, Firebase ML, TensorFlow Lite, Create ML, Clarafai, and Fritz AI to help you decide which platform is best for your project.