Amazon Machine Learning vs rasa NLU: What are the differences?

# Introduction
In this Markdown code, we will outline the key differences between Amazon Machine Learning (Amazon ML) and rasa NLU.

1. **Deployment Environment**: Amazon ML is a fully managed service provided by AWS, which simplifies building machine learning models whereas rasa NLU is an open-source framework that allows users to build custom natural language understanding models.
2. **Pricing Model**: Amazon ML charges users based on the usage of compute resources and the number of predictions made, while rasa NLU is free to use with no licensing costs, making it more cost-effective for small-scale projects.
3. **Integration Capabilities**: Amazon ML seamlessly integrates with other AWS services such as S3 and Redshift, enabling easy data processing and model training, whereas rasa NLU offers flexible integrations with various chatbot platforms and messaging channels.
4. **Model Customization**: Amazon ML provides pre-built algorithms for common machine learning tasks, limiting user customization options, while rasa NLU allows users to train models on specific domain data to achieve higher accuracy and precision.
5. **Scalability**: Amazon ML is designed for large-scale data processing and can handle massive datasets efficiently, whereas rasa NLU may face performance issues when dealing with extensive amounts of training data.
6. **Development Flexibility**: rasa NLU offers more control over the development process, allowing users to fine-tune models and experiment with different training strategies, whereas Amazon ML provides a more structured approach with limited customization possibilities.

In Summary, the key differences between Amazon Machine Learning and rasa NLU lie in their deployment environment, pricing model, integration capabilities, model customization, scalability, and development flexibility. Each platform offers distinct advantages and suitable use cases for specific project requirements.