aws-samples / aws-ml-data-lake-workshop
As customers move from building data lakes and analytics on AWS to building machine learning solutions, one of their biggest challenges is getting visibility into their data for feature engineering and data format conversions for using AWS SageMaker. In this workshop, we demonstrate best practices and build data pipelines for training data using…
☆62Updated 6 years ago
Alternatives and similar repositories for aws-ml-data-lake-workshop:
Users that are interested in aws-ml-data-lake-workshop are comparing it to the libraries listed below
- ☆52Updated 7 years ago
- Collection of Cloud Formation Templates, Lambda Scripts and sample code required to provision an AWS Data Lake for a ReInvent Lab Exercis…☆26Updated 5 years ago
- A collection of recommended practices to accelerate the building of secure data science environments in regulated environments.☆49Updated last year
- Learn how to build an end-to-end streaming architecture to ingest, analyze, and visualize streaming data in near real-time☆34Updated 2 years ago
- Open innovation with 60 minute cloud experiments on AWS☆87Updated 9 months ago
- A packaged Data Lake solution, that builds a highly functional Data Lake, with a data catalog queryable via Elasticsearch☆73Updated 4 years ago
- Reference Architectures for Datalakes on AWS☆79Updated 4 years ago
- This GitHub project provides a series of lab exercises which help users get started using the Redshift platform.☆53Updated 3 years ago
- A self-paced workshop designed to allow you to get hands on with building a real-time data platform using serverless technologies such as…☆22Updated 6 years ago
- Design pattern for orchestrating an incremental data ingestion pipeline using AWS Step Functions from an on premise location into an Amaz…☆28Updated 5 years ago
- A workshop demonstrating the capabilities of S3, Athena, Glue, Kinesis, and Quicksight.☆158Updated 4 years ago
- This solution helps you deploy Data Lake Infrastructure on AWS using CDK Pipelines.☆94Updated 2 years ago
- Design best practices for building scalable ETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) data processing pipelines using…☆17Updated 5 years ago
- AWS Workshop tutorial for building applications with Amazon AI Services☆31Updated 2 years ago
- Sample Jupyter Notebooks for Amazon Augmented AI (A2I)☆71Updated last year
- ☆73Updated last year
- ☆158Updated 11 months ago
- ☆67Updated 8 months ago
- Source code for the post, 'Getting Started with Data Analysis on AWS, using S3, Glue, Amazon Athena, and QuickSight'☆28Updated 4 years ago
- Deliver Pinpoint Campaigns Driven by Machine Learning on AWS SageMaker☆18Updated 6 years ago
- Repository for AWS Glue Workshop☆31Updated 2 years ago
- ☆26Updated 4 years ago
- CloudFormation templates and scripts to setup the AWS services for the workshop, Athena & Redshift Spectrum queries☆175Updated 4 years ago
- AI_ML_Workshops☆52Updated 4 years ago
- ☆22Updated 4 years ago
- ☆88Updated last year
- This solution helps you deploy ETL jobs on data lake using CDK Pipelines.☆67Updated 2 years ago
- A serverless framework for continuous machine learning pipeline automation☆14Updated 4 years ago
- ☆88Updated 2 years ago
- The objective of Cloud Builders' Day repository is to provide do-it-yourself lab guides for several AWS services including but not limite…☆11Updated 4 years ago