Aws AI services ar pre-trained ML services for our use case.

flowchart LR

subgraph Generative_AI_Tools
    A["SageMaker JumpStart"]
    B["Amazon Bedrock"]
    C["Amazon Q Business"]
    D["Amazon Q Developer"]
end

subgraph AI_Services

    subgraph Text_and_Documents
        E["Amazon Comprehend"]
        F["Amazon Translate"]
        G["Amazon Textract"]
    end

    subgraph Vision
        H["Amazon Rekognition"]
    end

    subgraph Search
        I["Amazon Kendra"]
    end

    subgraph Chatbots
        J["Amazon Lex"]
    end

    subgraph Speech
        K["Amazon Polly"]
        L["Amazon Transcribe"]
    end

    subgraph Recommendations
        M["Amazon Personalize"]
    end

end

subgraph ML_Platform
    N["Amazon SageMaker"]
end

These services have:

• Responsiveness and Availability: High availability and low latency for end-users.
• Redundancy and Regional Coverage: Deployed across multiple Availability Zones and AWS regions.
• Performance: Specialized CPU and GPUs for specific use-cases for cost saving.
• Token-based Pricing: We pay for what we use, no upfront costs, and can scale as needed.
• Provisioned throughput: For predictable workloads, cost savings and predictable performance.

Amazon Comprehend

It is a fully managed and serverless service for natural language processing - NLP and it uses machine learning to find insights and relationships in text:

• Language of the text
• Extracts key phrases, places, people, brands, or events
• Understands how positive or negative the text is.
• Analyzes text using tokenization and parts of speech.
• Automatically organizes a collection of text files by topic.

Sample use cases:

• Customer feedback analysis: Analyze customer reviews, social media posts, and survey responses to understand customer sentiment and identify common themes.
• Create and groups articles by topics that Comprehend will uncover.

Comprehend - Custom Classification

We can organize documents into categories (classes) that we define. For example, we can categorize customer emails so that we can provide guidance based on the type of the customer request.

It supports different doument types, including plain text, PDF, Word, images, and it can do:

• real-time analysis when new document comes in and treat it synchronously.
• Async Analysis for multiple documents (batch), Asychronous processing for large volumes of documents, and it can take hours to process.

Comprehend - Custom Entity Recognition

• NER - Extracts predefined, general-purpose entites like people, places, organizations, dates, and other standard categories, from text.
• Analyze text for specific terms and noun-based phrases.
• Extract terms like policy numbers, or phrases that imply a customer escalation, anything specific to our business.
• Train the model with custom data such as a list of the entities and documents that contain them.
• This can be real-time or async analysis.

Comprehend - Custom Models

• We can create custom models for entity recognition or document classification. It can be trained on our own data.
• Comprehend manages the model versioning.
• Custom models may be copied between AWS accounts. We can attach IAM policy to a model version, authrizing the other account to use it. The other account can then imports the model. These rules apply:
  • The other account must be in same region.
  • Need its ARN (identifier of the model), region, and optional KMS key.
  • Can be done from the Comprehend console.