SpaCy is one of my favourite NLP libraries. And I have been using spaCy to perform a lot of Named Entity Recognition (NER) tasks. Generally, we first need to load a spaCy pre-trained model of a specific language and fine-tune the model with our training dataset. The training process can be done offline with a local computer and we can even test the fine-tuned model performance by hosting it locally through Flask / Streamlit.
Although I have found many great tutorials on deploying a spaCy model locally with Flask / Streamlit, there are not many tutorials on how to deploy it on a larger scale, for example, how to deploy a spaCy model with AWS.
It’s a very interesting topic and after a great amount of work I summarized my solution into this article; hopefully it can be useful for people facing the same question.
In this article, I will explain my solution on how to deploy a custom spaCy model with AWS services including:
Here is my plan🧗🏻:
#aws #aws ecr #sagemaker #lambda
In this article, I will explain my solution on how to deploy a custom spaCy model with AWS services including: AWS ECR (Elastic Container Registry); AWS SageMaker; AWS Lambda; AWS S3 Bucket (Optional).