New features for Amazon SageMaker Pipelines and the Amazon SageMaker SDK

Favorite Amazon SageMaker Pipelines allows data scientists and machine learning (ML) engineers to automate training workflows, which helps you create a repeatable process to orchestrate model development steps for rapid experimentation and model retraining. You can automate the entire model build workflow, including data preparation, feature engineering, model training, model

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Shared by AWS Machine Learning September 28, 2022

Provision and manage ML environments with Amazon SageMaker Canvas using AWS CDK and AWS Service Catalog

Favorite The proliferation of machine learning (ML) across a wide range of use cases is becoming prevalent in every industry. However, this outpaces the increase in the number of ML practitioners who have traditionally been responsible for implementing these technical solutions to realize business outcomes. In today’s enterprise, there is

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Shared by AWS Machine Learning September 28, 2022

Build an AI-powered virtual agent for Genesys Cloud using QnABot and Amazon Lex

Favorite The rise of artificial intelligence technologies enables organizations to adopt and improve self-service capabilities in contact center operations to create a more proactive, timely, and effective customer experience. Voice bots, or conversational interactive voice response systems (IVR), use natural language processing (NLP) to understand customers’ questions and provide relevant

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Shared by AWS Machine Learning September 28, 2022

Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models

Favorite This post is co-written by Goktug Cinar, Michael Binder, and Adrian Horvath from Bosch Center for Artificial Intelligence (BCAI). Revenue forecasting is a challenging yet crucial task for strategic business decisions and fiscal planning in most organizations. Often, revenue forecasting is manually performed by financial analysts and is both

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Shared by AWS Machine Learning September 24, 2022