{"id":1588,"date":"2022-02-23T23:41:16","date_gmt":"2022-02-23T23:41:16","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2022\/02\/23\/how-kustomer-utilizes-custom-docker-images-amazon-sagemaker-to-build-a-text-classification-pipeline\/"},"modified":"2022-02-23T23:41:16","modified_gmt":"2022-02-23T23:41:16","slug":"how-kustomer-utilizes-custom-docker-images-amazon-sagemaker-to-build-a-text-classification-pipeline","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2022\/02\/23\/how-kustomer-utilizes-custom-docker-images-amazon-sagemaker-to-build-a-text-classification-pipeline\/","title":{"rendered":"How Kustomer utilizes custom Docker images & Amazon SageMaker to build a text classification pipeline"},"content":{"rendered":"
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This is a guest post by Kustomer\u2019s Senior Software & Machine Learning Engineer, Ian Lantzy, and AWS team Umesh Kalaspurkar, Prasad Shetty, and Jonathan Greifenberger.<\/em><\/p>\n

In Kustomer\u2019s own words, \u201cKustomer is the omnichannel SaaS CRM platform reimagining enterprise customer service to deliver standout experiences. Built with intelligent automation, we scale to meet the needs of any contact center and business by unifying data from multiple sources and enabling companies to deliver effortless, consistent, and personalized service and support through a single timeline view.\u201d<\/em><\/p>\n

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Kustomer wanted the ability to rapidly analyze large volumes of support communications for their business customers \u2014 customer experience and service organizations \u2014 and automate discovery of information such as the end-customer\u2019s intent, customer service issue, and other relevant insights related to the consumer. Understanding these characteristics can help CX organizations manage thousands of in-bound support emails by automatically classifying and categorizing the content. Kustomer leverages Amazon SageMaker<\/a> to manage the analysis of the incoming support communications via their AI based Kustomer IQ<\/a> platform. Kustomer IQ\u2019s Conversation Classification service is able to contextualize conversations and automate otherwise tedious and repetitive tasks, reducing agent distraction and the overall cost per contact. This and Kustomer\u2019s other IQ services have increased productivity and automation for its business customers.<\/p>\n

In this post, we talk about how Kustomer uses custom Docker images for SageMaker training and inference, which eases integration and streamlines the process. With this approach, Kustomer\u2019s business customers are automatically classifying over 50k support emails each month with up to 70% accuracy.<\/p>\n

Background and challenges<\/h2>\n

Kustomer uses a custom text classification pipeline for their Conversation Classification service. This helps them manage thousands of requests a day via automatic classification and categorization utilizing SageMaker\u2019s training and inference orchestration. The Conversation Classification training engine uses custom Docker images to process data and train models using historical conversations and then predicts the topics, categories, or other custom labels a particular agent needs in order to classify the conversations. Then the prediction engine utilizes the trained models with another custom docker image to categorize conversations, which organizations use to automate reporting or route conversations to a specific team based on its topic.<\/p>\n

The SageMaker categorization process starts by establishing a training and inference pipeline that can provide text classification and contextual recommendations. A typical setup would be implemented with serverless approaches like AWS Lambda<\/a> for data preprocessing and postprocessing because it has a minimal provisioning requirement with an effective on-demand pricing model. However, using SageMaker with dependencies such as TensorFlow, NumPy, and Pandas can quickly increase the model package size, making the overall deployment process cumbersome and difficult to manage. Kustomer used custom Docker images to overcome these challenges.<\/p>\n

Custom Docker images provide substantial advantages:<\/p>\n