Predict Shipping Delays with SageMaker Canvas : AIF-C01 AWS AI Practitioner - Part 16

Embark on an exciting hands-on journey to revolutionize logistics with cutting-edge AI! In this step-by-step tutorial, you'll learn how to create an end-to-end IT project for predicting the actual shipping days of products—without writing a single line of code, thanks to Amazon SageMaker AI Canvas.

What you'll achieve:
• Upload shipment and product data as .csv files from your computer to AWS S3.
• Configure a SageMaker AI domain, create a user profile, and set up your working environment.
• Seamlessly import shipment and product data into SageMaker Canvas and perform an inner join operation.
• Utilize SageMaker Canvas' Chat for Data Prep to generate an insightful graph comparing actual vs. expected shipping dates—automatically backed by AI-generated code.
• Drop unnecessary columns and set up regression analysis in SageMaker Data Wrangler for accurate predictions.
• Build, train, and export a machine learning model to predict the target variable: actual shipping days.
• Download the model output as a .csv file for actionable insights, and even run a single prediction directly in SageMaker Canvas.

This video is perfect for professionals in logistics and supply chain management, tech enthusiasts, and AI beginners eager to dive into no-code AI model building. Join today and empower yourself to optimize logistic operations with predictive analytics!
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Видео Predict Shipping Delays with SageMaker Canvas : AIF-C01 AWS AI Practitioner - Part 16 канала Cloud Guru Amit
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