Data Science and Artificial Intelligence

Implementing ML and Data Science on AWS

This Course Includes:

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Free One Year Access To Live Courses*
Certificate of completion
  • Introduction to AWS for ML and Data Science
  • Data Ingestion and Storage
  • Data Preprocessing and Transformation
  • AWS SageMaker
  • Model Training and Evaluation
  • Model Deployment and Hosting
  • Automated ML with AWS SageMaker Autopilot
  • Data Visualization and Analysis
  • Serverless Architectures for ML
  • Managed Databases and Data Warehousing
  • Advanced Analytics and AI Services
  • Security and Compliance in AWS
  • Cost Management
  • Monitoring and Logging
  • Case Studies and Best Practices

Course Curriculum & Resources

  • Overview of AWS services relevant to ML and data science
  • Understanding the AWS cloud infrastructure

  • Data ingestion strategies using AWS services
  • Storing and managing data in AWS, including S3 and other storage services

  • Data cleaning and preprocessing using AWS tools
  • Transformation and feature engineering using AWS services

  • Introduction to Amazon SageMaker for building, training, and deploying ML models
  • Configuring and using SageMaker notebooks

  • Training ML models using SageMaker
  • Hyperparameter tuning and optimization
  • Model evaluation and validation

  • Deploying models to production using SageMaker
  • Setting up endpoints for real-time inference
  • Batch inference using SageMaker

  • Using SageMaker Autopilot for automated machine learning
  • Understanding the AutoML process on AWS

  • Utilizing AWS services for data visualization (e.g., Amazon QuickSight)
  • Analyzing and exploring data using AWS tools

  • Leveraging AWS Lambda for serverless ML applications
  • Building serverless architectures with AWS services

  • Using Amazon RDS and Amazon Redshift for managed databases
  • Data warehousing solutions on AWS

  • Leveraging AWS AI services (e.g., Amazon Comprehend, Amazon Polly)
  • Implementing advanced analytics using AWS tools

  • Implementing security best practices for ML and data science on AWS
  • Ensuring compliance with regulations

  • Understanding cost considerations for ML and data science workloads
  • Optimizing costs with AWS pricing models

  • Monitoring ML models and data pipelines on AWS
  • Logging and troubleshooting using AWS CloudWatch

  • Analyzing real-world case studies of ML and data science on AWS
  • Best practices for successful implementations

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    Implementing ML and Data Science on AWS

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