A Spark library for Amazon SageMaker.

This code is used to build & run a Docker container for performing predictions against a Spark ML Pipeline.

The SageMaker Spark Container is a Docker image used to run data processing workloads with the Spark framework on Amazon SageMaker.

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Deploy a Spark pipeline using Amazon Sagemaker.

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Enable local mode for SageMaker Processing with Spark.

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The **AWS SageMaker + Snowflake ML Pipeline** is a fully production-grade, end-to-end machine learning workflow designed to ingest large-scale data from Snowflake, perform feature engineering with Apache Spark, and train, tune, and deploy models on AWS SageMaker—all orchestrated and versioned with CI/CD, Terraform, and Ansible.

Running Spark on AWS EMR to perform KMEANS and XGBoost on MNIST dataset

Emotional Sentiment Analysis using AWS SageMaker Spark

This repository contains code for end-to-end sparkml on sagemaker

ALS based recommendation Engine Build on Apache spark & served on AWS Sagemaker

Learning objectives Top-N recommender architectures Types of recommenders Python basics for working with recommenders Evaluating recommender systems Measuring your recommender Reviewing a recommender engine framework Content-based filtering Neighborhood-based collaborative filtering Matrix factorization methods Deep learning basics Applying deep learning to recommendations Scaling with Apache Spark, Amazon DSSTNE, and AWS SageMaker Real-world challenges and solutions with recommender systems Case studies from YouTube and Netflix Building hybrid, ensemble recommenders

Training session with Spark & Sagemaker

Template to read tables from S3 with Spark within AWS Sagemaker

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:cloud: This code is used to build & run a Docker container for performing predictions against a Spark ML Pipeline

SageMaker-compatible hosting container for Spark ML models

Distributed deep learning system for early-stage leukemia detection using AWS, Spark, and SageMaker

Exploration and processing of large amounts of ADS-B flight data using Apache Spark. Also includes basic ML modeling using Amazon Sagemaker.

A big data project that utilizes E3, Athena, EMR, SageMaker and QuickSight on AWS to build Random Forest and xgBoost model in Spark and SQL that predict the CTR of ads on a large relational database.

A conda-smithy repository for sagemaker-studio-sparkmagic-lib.

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Sagemaker Spark Customized repo for spark-nlp

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