AWS Machine Learning Tool Stack

AWS Core Services (General Knowledge)

1. Amazon S3: Storage for data, models, and datasets.

2. Amazon EC2: Compute services for running ML workloads.

3. AWS Lambda: Serverless execution for lightweight ML tasks.

4. AWS CloudFormation: Infrastructure as code for automating resource deployment.

5. AWS IAM: Managing roles and permissions for ML resources.

6. Amazon RDS: Relational databases for structured data.

7. AWS Glue: ETL service to prepare and transform data.

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Machine Learning-Specific AWS Services

1. Amazon SageMaker:

   • Build, train, and deploy ML models.

   • Includes SageMaker Studio, SageMaker Pipelines, and SageMaker Autopilot.

2. Amazon SageMaker Ground Truth: Data labeling for supervised learning.

3. Amazon SageMaker Model Monitor: Monitor deployed models for bias or drift.

4. Amazon SageMaker Clarify: Detect bias in datasets and models.

5. Amazon SageMaker Neo: Optimize models for deployment on edge devices.

6. Amazon SageMaker Feature Store: Centralized feature storage for ML models.

7. Amazon SageMaker Data Wrangler: Prepare and explore data.

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Data and Analytics Services

1. Amazon Athena: Querying data directly from S3 using SQL.

2. Amazon Redshift: Data warehousing for analytics.

3. AWS Kinesis: Streaming data for real-time analytics.

4. Amazon QuickSight: Visualization and reporting.

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AI Services (Pre-Trained Models)

1. Amazon Rekognition: Image and video analysis.

2. Amazon Comprehend: Natural Language Processing (NLP).

3. Amazon Polly: Text-to-speech.

4. Amazon Lex: Conversational interfaces (chatbots).

5. Amazon Transcribe: Automatic speech-to-text.

6. Amazon Translate: Language translation.

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DevOps and Deployment Tools

1. AWS CodePipeline: Automate ML model workflows.

2. AWS CodeBuild: Build and test code for ML applications.

3. AWS Elastic Beanstalk: Simplified deployment of ML applications.

4. Amazon Elastic Kubernetes Service (EKS): Deploy containerized ML workloads.

5. Amazon Elastic Container Service (ECS): Run containerized models and apps.

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Security and Monitoring Tools

1. AWS CloudWatch: Monitor resources and set alarms for ML pipelines.

2. AWS Key Management Service (KMS): Encrypt data at rest.

3. AWS Secrets Manager: Securely store and manage API keys and credentials.

4. AWS Config: Track resource configurations and compliance.

5. AWS Shield: Protect ML endpoints against DDoS attacks.

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Other Noteworthy Tools

1. AWS Deep Learning AMIs: Pre-configured environments for ML frameworks.

2. AWS DeepRacer: Autonomous racing simulator for RL learning.

3. AWS Snowball: Transfer large datasets to AWS securely.

4. AWS DataSync: Automate and accelerate data transfers.

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