Update How_Conxai_uses_Knative_Eventing_to_provide_AI_APIs_in_the_construction_industry.md

Author: tikr7

blog/docs/articles/How_Conxai_uses_Knative_Eventing_to_provide_AI_APIs_in_the_construction_industry.md

@@ -16,7 +16,7 @@ KServe is a standard Model Inference Platform on Kubernetes that leverages Knati
 
 ## Our blurring product step by step
 
-First, the customer uploads an image to be blurred via our API, which saves the image on S3. The S3 event is routed to our AWS EventBridge. The TriggerMesh AWSEventBridgeSource automatically configures an Amazon SQS queue and EventBridge Rule to forward the S3 event into that Amazon SQS queue. Because TriggerMesh has been deprecated, we will switch to Knative Serving [IntegrationSource](https://knative.dev/blog/articles/consuming_sqs_data_with_integrationsource/) approach with AWS SQS in the future. Then it passes that event into the Knative Broker which is backed by Amazon MSK - Kafka. Since we heavily rely on S3 key/file names, we wrote our own router service in Golang which modifies the EventType using regex patterns and sends it back to the Knative Broker.
+First, the customer uploads an image to be blurred via our API, which saves the image on S3. The S3 event is routed to our AWS EventBridge. The TriggerMesh AWSEventBridgeSource automatically configures an Amazon SQS queue and EventBridge Rule to forward the S3 event into that Amazon SQS queue. Because TriggerMesh has been deprecated, we will switch to Knative Eventing [IntegrationSource](https://knative.dev/blog/articles/consuming_sqs_data_with_integrationsource/) approach with AWS SQS in the future. Then it passes that event into the Knative Broker which is backed by Amazon MSK - Kafka. Since we heavily rely on S3 key/file names, we wrote our own router service in Golang which modifies the EventType using regex patterns and sends it back to the Knative Broker.
 
 Another Knative Trigger moves it into our transformer which handles preprocessing first. As the next step, the transformer calls the predictor (Nvidia Triton) directly over HTTP. We use KServe's "Collocate transformer and predictor in same pod" [feature](https://kserve.github.io/website/0.13/modelserving/v1beta1/transformer/collocation/) to maximize inference speed and throughput. After receiving model results, the transformer does the postprocessing and finally saves the model results on S3.