For machines to comprehend natural language it requires complex data processing and advanced machine learning algorithms. Depending on the language and the type of data the process can vary, as well as the algorithms. It is also not trivial to determine which machine learning method to apply based on a given dataset or task.
E.g., Numerous machine learning methods and NLP pipelines can be used to classify text documents. However, do you keep questioning the following
🤔 Which one will give the best results?
🤔 How much data will each process need to train?
🤔 Which pipeline will scale best?
🤔 How complex are each of them to put in production?
Additionally, the entire language processing stack might be different for different languages. Even if you have all the components together there is no guarantee you will get the best results.
To address all these challenges we came up with AutoNLP. An algorithm that figures out which pipeline, which features, and which model will give the best results.
You only need to give it data, it does everything else for you!.
Since creating datasets is itself a difficult task, we have made our AutoNLP highly data efficient. Different apps have a different variants of AutoNLP and they also have different minimum data requirements. E.g., Natural Language Understanding (NLU) needs 20 training examples, and Neural Machine Translation needs 400-500 training examples.
No Machine learning or data science knowledge required
This means you don't need to have any machine learning or data science knowledge to train your AI models. You focus on the business problems you are solving and let our platform take care of all the language processing and machine learning.