AWS ML Deployment, Storage, Security, Monitoring, and Managed AI Services
In this presentation, I explain *AWS Machine Learning Deployment, Storage, Security, Monitoring, and Managed AI Services* as part of my AWS Machine Learning Associate exam preparation. This video covers how machine learning models are deployed and managed on AWS, including production-ready ML system requirements, scalable infrastructure, reliable inference endpoints, workflow automation, storage options, streaming services, monitoring, security, governance, and cost optimization. I also discuss AWS services such as Amazon ECS, Amazon EKS, AWS Lambda, Amazon SageMaker endpoints, SageMaker Pipelines, AWS Step Functions, Amazon S3, Amazon EBS, Amazon EFS, Amazon FSx, Amazon Kinesis, Amazon MSK, Amazon CloudWatch, AWS Cost Explorer, AWS KMS, AWS Secrets Manager, AWS Macie, IAM, and AWS CloudTrail. Additionally, this presentation introduces AWS Managed AI Services such as Amazon Comprehend, Amazon Rekognition, Amazon Lex, Amazon Textract, Amazon Personalize, Amazon Translate, Amazon Transcribe, Amazon Kendra, Amazon Fraud Detector, Amazon Augmented AI, and Amazon Mechanical Turk. Overall, this video explains how AWS supports the complete machine learning lifecycle, from deployment and monitoring to security, governance, and AI-powered application development. #AWSMachineLearning #AWSMLAssociate #AWSCertification #MachineLearning #MLOps #AmazonSageMaker #AWSCloud #CloudComputing #ModelDeployment #ModelMonitoring #AWSLambda #AmazonS3 #AmazonCloudWatch #AWSAI #ManagedAIServices #DataScience
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