Combine Amazon Translate with Elasticsearch and Skedler to build a cost-efficient multi-lingual omnichannel customer care – Part 1 and 2 – Skedler Blog

I’ve just published a new blog post on the Skedler Blog.

In this two-part blog post, we are going to present a system architecture to translate customer inquiries in different languages with AWS Translate, index this information in Elasticsearch 6.2.3 for fast search, visualize the data with Kibana 6.2.3, and automate reporting and alerting using Skedler.

The components that we are going to use are the following:

  • AWS API Gateway
  • AWS Lambda
  • AWS Translate
  • Elasticsearch 6.2.3
  • Kibana 6.2.3
  • Skedler Reports and Alerts

System architecture:

You can read the full post – Part 1 – here: Combine Amazon Translate with Elasticsearch and Skedler to build a cost-efficient multi-lingual omnichannel customer care – Part 1.

Part 2 – here: Combine Amazon Translate with Elasticsearch and Skedler to build a cost-efficient multi-lingual omnichannel customer care – Part 2 of 2.

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Extract business insights from audio using AWS Transcribe, AWS Comprehend and Elasticsearch – Part 1 and 2- Skedler Blog

I’ve just published a new blog post on the Skedler Blog.
In this two-part blog post, we are going to present a system architecture to convert audio and voice into written text with AWS Transcribe, extract useful information for quick understanding of content with AWS Comprehend, index this information in Elasticsearch 6.2 for fast search and visualize the data with Kibana 6.2. In Part I, you can learn about the key components, architecture, and common use cases. In Part II, you can learn how to implement this architecture.

The components that we are going to use are the following:

  • AWS S3 bucket
  • AWS Transcribe
  • AWS Comprehend
  • Elasticsearch 6.2
  • Kibana 6.2
  • Skedler Reports and Alerts

System architecture:

You can read the full post – Part 1 – here: Extract business insights from audio using AWS Transcribe, AWS Comprehend and Elasticsearch – Part 1.

Part 2 – here: Extract business insights from audio using AWS Transcribe, AWS Comprehend and Elasticsearch – Part 1.

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Application Performance Monitoring (APM) with Elasticsearch 6.1.1

In June 2017 Elastic joined forces with Opbeat an application performance monitoring (APM) company. Read the official blog post here: Welcome Opbeat to the Elastic Family.

Adding APM (Application Performance Monitoring) to the Elastic Stack is a natural next step in providing our users with end-to-end monitoring, from logging, to server-level metrics, to application-level metrics, all the way to the end-user experience in the browser or client.

Elastic APM consists of three components:

  • Agents: libraries that run inside of your application process and automatically measure the duration of requests to your service and things like database queries, cache calls, external http requests and errors
  • The APM server (written in Golang) that processes data from agents and stores the data in Elasticsearch
  • Kibana UI: dashboards that gives you an instant overview of application response times, requests per minutes, error occurrences and more.

The APM server and the agents (right now available only for Python and NodeJS) are open source:

Read more about it here: Starting Down the Path of APM for the Elastic Stack

In this post we are not going to see how to install the APM server, you can find the instructions here: Open Source Application Performance Monitoring.

Once the APM Server is installed and started we can monitor the performance of our application. In this example we will see a Python Flask application.

Install the Python APM library:

Initialize the client:

Within the Flask route you can log some messages:

or exceptions:

Here is how the monitoring looks like in Kibana:

You can see the details of each request by clicking on it:

 

I really like the APM feature fully integrated with the Elastic Stack. I will integrate it within my Flask/Django applications.
If you want to read more about the new APM feature:

If you want to read more about this topic: Application Performance Monitoring with Elasticsearch 6.1, Kibana and Skedler Alerts.

How to combine Text Analytics and Search using AWS Comprehend and Elasticsearch 6.0 – Skedler Blog

I published a new blog post on the Skedler Blog.
In the post we are going to see how to combine text analytics and search using AWS Comprehend and Elasticsearch 6.0.

The components that we are going to use are the following:

  • Amazon S3 bucket and Amazon Simple Queue Service
  • Amazon Comprehend
  • Elasticsearch 6.0
  • Elasticsearch Ingest Attachment Processor Plugin
  • Elasticsearch Ingest Geoip Processor Plugin
  • Kibana
  • Skedler Reports and Alerts

System Architecture:


 
You can read the full post here: How to combine Text Analytics and Search using AWS Comprehend and Elasticsearch 6.0

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Machine learning with Amazon Rekognition and Elasticsearch – Skedler Blog

I published a new blog post on the Skedler Blog.
In the post we are going to see how to build a machine learning system to perform the image recognition task and use Elasticsearch as search engine to search for the labels identified within the images.

The components that we used are the following:

  • Elasticsearch
  • Kibana
  • Skedler Reports and Alerts
  • Amazon S3 bucket
  • Amazon Simple Queue Service (eventually you can replace this with AWS Lambda)
  • Amazon Rekognition

System Architecture:

 

You can read the full post here: Machine learning with Amazon Rekognition and Elasticsearch

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