Anil Sener's repositories
amazon-forecast-samples
Notebooks and examples on how to onboard and use various features of Amazon Forecast.
amazon-neptune-samples
Samples and documentation for using the Amazon Neptune graph database service
amazon-redshift-utils
Amazon Redshift Utils contains utilities, scripts and view which are useful in a Redshift environment
amazon-sagemaker-developer-guide
The open source version of the Amazon SageMaker docs
amazon-sagemaker-examples
Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker
amazon-sagemaker-stock-prediction
Workshop to demonstrate how to apply NN based algorithms to stock market data and forecast price movements.
amazon-serverless-datalake-workshop
A workshop demonstrating the capabilities of S3, Athena, Glue, Kinesis, and Quicksight.
awesome-serverless
:cloud: A curated list of awesome services, solutions and resources for serverless / nobackend applications.
aws-ai-ml-workshop-kr
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
aws-appsync-calorie-tracker-workshop
Serverless application that demonstrate how to use AWS AppSync and Amazon Neptune to build a realtime, data driven application.
aws-bosch-sagemaker-kaggle
AWS Bosch Kaggle Demo
aws-etl-orchestrator
A serverless architecture for orchestrating ETL jobs in arbitrarily-complex workflows using AWS Step Functions and AWS Lambda.
aws-glue-samples
AWS Glue code samples
bahir-flink
Mirror of Apache Bahir Flink
face_recognition
The world's simplest facial recognition api for Python and the command line
grafana-aws-cloudwatch-dashboards
:cloud: 20+ Grafana dashboards for AWS CloudWatch metrics: EC2, Lambda, S3, ELB, EMR, EBS, SNS, SES, SQS, RDS, EFS, ElastiCache, Billing, API Gateway, VPN, ...
h2o3-sagemaker
Integrating H2O-3 AutoML with Amazon Sagemaker
kafka-connect-dynamodb
A Kafka Connect Source Connector for DynamoDB
kinesis-sql
Kinesis Connector for Structured Streaming
ml_lifecycle_lab
A collection of notebooks for walking through the typical ML lifecycle from data cleaning through to model hosting using Amazon SageMaker.
sagemaker-mxnet-container
This support code is used for making the MXNet framework run on Amazon SageMaker.
serverless-sagemaker-orchestration
This example shows how to build a serverless pipeline to orchestrate the continuous training and deployment of a linear regression model for predicting housing prices using Amazon SageMaker, AWS Step Functions, AWS Lambda, and Amazon CloudWatch Events.
spark-redshift
Redshift data source for Apache Spark
sparkprophet
Sample application running fbprophet using spark