Implement fast, and rich NLP features using Elasticsearch

Steps

Define NLP Requirements: The first step is to clearly define the NLP requirements and objectives, such as identifying the specific features and functionalities that need to be implemented using Elasticsearch.

Plan Elasticsearch Infrastructure: This step involves planning and provisioning the required Elasticsearch infrastructure, including the necessary hardware and software resources needed to support the NLP features.

Decide on Pipeline Approach: This step involves deciding on the approach for building the Elasticsearch pipelines that will be used to process and analyze the text data. The two options mentioned are using YAML-based or wizard-based pipelines.

Develop NLP Features: In this step, the actual development of the NLP features using Elasticsearch takes place. This may involve tasks such as creating Elasticsearch indices, defining mapping and data types, and developing queries and aggregations.

Implement Continuous Integration: This step involves setting up a Continuous Integration (CI) pipeline that will automate the process of assembling the code changes into compressed artifacts, ensuring that any changes to the code are thoroughly tested before being integrated into the main codebase.

Implement Continuous Delivery for Elasticsearch Indices: Finally, the Continuous Delivery (CD) pipeline is set up to automate the deployment of the NLP features to the Elasticsearch indices. This ensures that the new features are safely and seamlessly deployed to the production environment.