Data-Science-On-Aws

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Data-science-on-aws is an open-source resource for learning data science on AWS. It provides examples for building AI/ML pipelines using Kubeflow, Amazon EKS, and SageMaker, accompanying an O'Reilly book.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is data-science-on-aws?

Data-science-on-aws is an open-source resource designed to educate users on implementing AI and Machine Learning solutions within the Amazon Web Services (AWS) ecosystem. It provides comprehensive examples for constructing end-to-end AI/ML pipelines, leveraging powerful tools such as Kubeflow, Amazon EKS, and Amazon SageMaker. The resource is structured around an O'Reilly book, offering practical, hands-on demonstrations. Users will learn to train and tune BERT models for natural language processing, perform hyper-parameter tuning, A/B testing, and set up real-time streaming analytics. It covers data ingestion, exploration, preparation, model training, optimization, deployment, and security, making it ideal for those looking to master data science workflows on AWS.

Best used for

Ideal for developers who need to learn practical data science on AWS, build end-to-end AI/ML pipelines, and deploy machine learning models. Especially valuable for those looking to gain hands-on experience with Amazon SageMaker, Kubeflow, and Amazon EKS.

Common actions

learn machine learning

build AI pipelines

deploy models

analyze data

automated workflowlow-code/no-codeworkflowsopen-sourcedeepfakecollaborationface swapping"AI Agents"github copilot

Capabilities

Key features

AI/ML pipeline examples
BERT model training
Hyper-parameter tuning
A/B testing deployment
Streaming analytics setup
SageMaker integration

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of AI/ML models can I learn to build with this resource?

You will learn to build and tune advanced models, specifically focusing on a BERT-based NLP text classifier to predict star ratings from product reviews. The resource also covers techniques like hyper-parameter tuning and A/B testing for model optimization and deployment.

What AWS services are covered in the examples?

The examples extensively cover Amazon SageMaker for various tasks including training, processing, feature store, model registry, and hosting. It also integrates with Amazon Athena, Kinesis, and other AWS services for a comprehensive data science workflow.

Do I need an AWS account to use this resource?

Yes, you will need an active AWS account to follow along with the book examples and run the provided code. Instructions are included for creating and activating an AWS account if you don't already have one.

Does this resource cover real-time data processing?

Yes, the resource includes examples for setting up a real-time, streaming analytics and data science pipeline. This involves performing window-based aggregations and anomaly detection using services like Amazon Kinesis and SageMaker.

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Data & Analytics › Data Pipelines & Integration Coding & Development › DevOps & Infrastructure

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