There are several types of AI services
February 2, 2023AI Open Source Comparison
February 2, 2023
OpenAI
Pros
- High accuracy for a variety of tasks
- Easy to use and get started with
- Reach wide and diverse range of applications
- Can create completely custom training routines
Cons
- Limited deployment options
- Requires extensive knowledge of the underlying code to modify or customize
- Can be a bit slow and consuming regarding resources
Teachable Machine
Pros
- Web-based environment
- Easy drag & drop customization
- Versatile set of tools to choose from
- Runs on all types of hardware
Cons
- Limited documentation
- Limited transfer learning capabilities
- Easy to get carried away with customization
- No support for deployment
Scikit-learn
Pros
- Very simple interface
- Supports several different machine learning models
- Easy to implement and customize
- Can work with very large datasets
Cons
- Limited transfer learning capabilities
- Poorly documented
- Poor visualization library
- Limited support for deep learning
TensorFlow
Pros
- Supports a wide variety of commonly used deep learning models
- Extremely large online community
- Easy to maintain code
- Versatile deployment options
Cons
- Poorly documented API
- Unintuitive to new users
- Resource intensive
- Requires extensive knowledge to create custom models
Keras
Pros
- Very user friendly API
- Built-in support for transfer learning
- Easy to maintain code
- High accuracy for a variety of tasks
Cons
- Limited support for custom models
- Limited deployment options
- Poorly documented API
- Limited visualization capabilities
PyTorch
Pros
- Variable length sequences support
- Very intuitive API
- Graph-based execution platform
- Built-in support for deployment
Cons
- Poorly documented API
- Performance is worse on smaller datasets
- Limited transfer learning capabilities
- Limited support for custom models
AutoKeras
Pros
- Easy to use and get started with
- Automates most of the tedious parts of machine learning
- High accuracy on a variety of tasks
- Supports most types of data
Cons
- Limited control over the model and parameters
- Limited transfer learning capabilities
- Not suitable for more complex tasks
- Poorly documented API
H2O.ai
Pros
- Easy to use and get started with
- Automates most of the tedious parts of machine learning
- Good scalability for large datasets
- Very user friendly API
Cons
- Limited transfer learning capabilities
- Limited control over the model and parameters
- Poorly documented API
- Not suitable for complex tasks
