guyernest

{
    "version": "1.0",
    "metadata": {
      "name": "Customer Sentiment Analysis Dataset",
      "description": "Analyze customer feedback sentiment (positive, negative, neutral) with product categories for e-commerce and retail businesses.",
      "created_date": "2025-01-14",
      "dataset_type": "classification",
      "total_samples": 20,
      "export_timestamp": "2025-01-14T10:00:00Z",
      "tags": ["sentiment", "classification", "customer-feedback", "e-commerce"],

guyernest

{
  "version": "1.0",
  "metadata": {
    "created_date": "2025-08-05",
    "total_samples": 15,
    "dataset_type": "email_classification",
    "description": "Sample dataset for testing email classification as 'For Action' or 'FYI' with department metadata",
    "export_timestamp": "2025-08-05T20:00:00Z"
  },
  "samples": [

guyernest

graph TB
    subgraph "Enterprise Security Layers"
        subgraph "Infrastructure Security"
            APIGW[API Gateway<br/>- Rate limiting<br/>- DDoS protection<br/>- SSL termination]
            Auth[Lambda Authorizer<br/>- JWT validation<br/>- Enterprise claims<br/>- 5-min caching]
        end
        
        subgraph "Identity & Access"
            Cognito[AWS Cognito<br/>- Enterprise SSO<br/>- MFA enforcement<br/>- Group-based access]

guyernest

graph LR
    subgraph "AI Client Layer"
        AI[AI Tools<br/>Claude, GPT, etc.]
    end

    subgraph "Security Gateway"
        Gateway[API Gateway<br/>+ Lambda Authorizer]
    end

guyernest

sequenceDiagram
    participant Enterprise as Enterprise User
    participant AI as AI Client<br/>(Claude/Inspector)
    participant APIGW as API Gateway
    participant OAuth2 as OAuth2 Proxy
    participant Auth as Lambda Authorizer
    participant Cognito as AWS Cognito
    participant MCP as MCP Server
    participant Data as Enterprise Data

guyernest

{
  "Comment": "AI Agent Implementation",
  "QueryLanguage": "JSONata",
  "StartAt": "Call LLM",
  "States": {
    "Call LLM": {
      "Type": "Task",
      "Resource": "arn:aws:states:::lambda:invoke",
      "Retry": [
        {

guyernest

select * from

(select cast(category as integer) as item_num_history, date_trunc('day', calendar_date) as sales_day, sum(sales) as demand
 
from forecast.compressed_data group by 1,2) as a

right join

(select *, date_trunc('day', from_iso8601_timestamp(date)) as forecast_day, cast(item_id AS integer) as item_num_forecast
from forecast.output_train_1217_category) as b

guyernest

# Request for a forecast based on time and product filter
forecastResponse = forecastquery.get_forecast(
    PredictorName=predictorName,
    StartDate='2018-01-01T00:00:00Z',
    EndDate='2018-01-10T00:00:00Z',
    Interval="day",
    Filters={"item_id":"012"}

)

guyernest

forecast.deploy_predictor(PredictorName=predictorName)

while True:
    deployedPredictorStatus = forecast.describe_deployed_predictor(
      PredictorName=predictorName
    )['Status']
    print(deployedPredictorStatus)
    if deployedPredictorStatus != 'ACTIVE' and deployedPredictorStatus != 'FAILED':
        sleep(30)
    else:

guyernest

predictorName= project+'_ARIMA'

# We will create forecast prediction for two months (60 days)
forecastHorizon = 60

# Starting the predictor creation job
createPredictorResponse=forecast.create_predictor(
    RecipeName=recipe,
    DatasetGroupName= datasetGroupName ,
    PredictorName=predictorName,