Author: AI Generated Robotic Content

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. Valuable data in organizations is stored in both structured and unstructured repositories. An enterprise search solution should …

Read more “Index your Dropbox content using the Dropbox connector for Amazon Kendra”

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 a need for machine learning to …

Read more “Provision and manage ML environments with Amazon SageMaker Canvas using AWS CDK and AWS Service Catalog”

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 tuning, and model validation, and catalog …

Read more “New features for Amazon SageMaker Pipelines and the Amazon SageMaker SDK”

Data scientists often train their models locally and look for a proper hosting service to deploy their models. Unfortunately, there’s no one set mechanism or guide to deploying pre-trained models to the cloud. In this post, we look at deploying trained models to Amazon SageMaker hosting to reduce your deployment time. SageMaker is a fully …

Read more “Reduce the time taken to deploy your models to Amazon SageMaker for testing”

Volkswagen strives to design beautiful, performant, and energy efficient vehicles. This entails an iterative process where designers go through many design drafts, evaluating each, integrating the feedback, and refining.  For example, a vehicle’s drag coefficient—its resistance to air—is one of the most important factors of energy efficiency. Thus, getting estimates of the drag coefficient for …

Read more “How Volkswagen and Google Cloud are using machine learning to design more energy-efficient cars”