Safe blue/green deployment of Amazon SageMaker endpoints using AWS CodePipeline, CodeBuild and CodeDeploy.
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Updated
Oct 13, 2022 - Jupyter Notebook
Safe blue/green deployment of Amazon SageMaker endpoints using AWS CodePipeline, CodeBuild and CodeDeploy.
A Spark library for Amazon SageMaker.
(Unofficial) curated list of awesome workshops found around in the internet. As we all have been there, finding that workshop that you have just attended shouldn't be hard. The idea is to provide an easy central repository, in a collaborative way.
Open innovation with 60 minute cloud experiments on AWS
Amazon SageMaker Local Mode Examples
Hands-on demonstrations for data scientists exploring Amazon SageMaker
Docker images that replicate the Amazon SageMaker Notebook instance.
A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)
Implementation of Protein Classification based on subcellular localization using ProtBert(Rostlab/prot_bert_bfd_localization) model from Hugging Face library, based on BERT model trained on large corpus of protein sequences.
MLOps workshop with Amazon SageMaker
Deploy Generative AI models from Amazon SageMaker JumpStart using AWS CDK
Amazon SageMaker Managed Spot Training Examples
Running your TensorFlow models in Amazon SageMaker
Snowflake Guide: Building a Recommendation Engine Using Snowflake & Amazon SageMaker
@DeepLearning.AI Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It has helped me to develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.
⛳️ PASS: Amazon Web Services Certified (AWS Certified) Machine Learning Specialty (MLS-C01) by learning based on our Questions & Answers (Q&A) Practice Tests Exams.
Hands-on end-to-end workshop to explore Amazon SageMaker.
End to end Machine Learning with Amazon SageMaker
Demonstration of Natural Language Query (NLQ) of an Amazon RDS for PostgreSQL database, using SageMaker JumpStart, Amazon Bedrock, LangChain, Streamlit, and Chroma.
This solution combines Amazon Pinpoint with Amazon SageMaker to help automate the process of collecting customer data, predicting customer churn using ML, and maintaining a tailored audience segment for messaging.
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