Using Python with AWS

Transcript

1. AWS Python   biwacode 2018.09 Python
    
    
      2018. 9. 8 (Sat)
2. 2 2 2
3. ( 3 1 ) 2
4. PowerBudget PowerBudget
    4
5. 
     
6. 
      
   
7. 
      
   
8. 
      
   
9. 
      
   
10. 
        
    
11. AWS Python 1
12. Amazon S3 2 1 0.023USD/GB
13. S3 Select S3 1 CSV JSON Parquet 3 1
14. 
     S3 Select
15. 
     S3 Select
16. 
     S3 Select
17. 
     S3 Select
18. 
     S3 Select
19. AWS Cloud9 9 (IDE) 9 9 AWS 1
20. 
    
21. AWS Lambda FaaS (Function as a Service) AWS 1 2 (Glue)
22. 
    
    AWS Lambda
23. AWS Glue ETL3 GUI ETL ETL 2 PySpark Scala 2 Zeppelin
24. 
     AWS Glue
25. 
     AWS Glue
26. 
    
27. 
     AWS Glue
28. 
     AWS Glue
29. Amazon SageMaker 2 9 AWS
30. 
    Amazon SageMaker – 
31. Amazon SageMaker – AWS 3 Jupyter Notebook S3 AWS 1
32. 
    Amazon SageMaker –  containers = {'ap-northeast-1': '501404015308.dkr.ecr.ap-northeast-1.amazonaws.com/xgboost:latest'} xgb = sagemaker.estimator.Estimator(containers[boto3.Session().region_name], role, train_instance_count=1, train_instance_type='ml.m4.4xlarge', output_path='s3://{}/{}/output'.format(bucket, prefix), sagemaker_session=sess) xgb.set_hyperparameters(eta=0.1, objective='multi:softmax’, num_class=10, num_round=25) xgb.fit({'train': train_input, 'validation': valid_input}, job_name = 'xgboost-classification')
33. Amazon SageMaker – Jupyter Notebook 3 3 API 3
34. Amazon SageMaker – 3 xgb_predictor = xgb.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge’) Docker 4
35. 
     Amazon SageMaker – 
36. Amazon Lex Amazon Polly Amazon Rekognition Text to Speech 3 (Amazon Echo(Alexa) Twilio ) Amazon Translate Amazon Comprehend 6 3 Video (NLP) 3
37. Amazon Connect 7 3 ACD IVR GUI
38. 
     Amazon Connect
39. 
     Amazon Connect    Python Lambda   
40. Amazon Connect 04
41. 41