Arich Infotech

AWS Machine Learning Engineer

Duration in Hours : 40

Duration in Days: 20

AWS Machine Learning Engineer

Course Code: AWSMLE20CS

20 Hours of Theory

20 Hours of Lab

  1. Overview of ML concepts (supervised, unsupervised learning)
  2. AWS services for ML (SageMaker, EC2, S3, Lambda)
  1. Data cleaning and transformation using AWS Glue
  2. Feature extraction and selection
  3. Storing and managing datasets with Amazon S3 and AWS Glue
  1. Setting up Amazon SageMaker environment
  2. Data labeling with Amazon SageMaker Ground Truth
  3. SageMaker Notebooks
  1. Algorithms in Amazon SageMaker (XGBoost, linear regression, etc.)
  2. Training and tuning ML models with SageMaker
  3. Model hyperparameter tuning using SageMaker’s built-in features
  1. Building deep learning models with TensorFlow or PyTorch on AWS
  2. Training large-scale models on GPU instances (Amazon EC2)
  3. Amazon Elastic Inference and AWS Batch for scalable training
  1. Model evaluation metrics (accuracy, precision, recall, etc.)
  2. Cross-validation and split strategies
  3. Analyzing model performance with SageMaker Experiments
  1. Deploying models to production using Amazon SageMaker endpoints
  2. Real-time vs batch inference
  3. Automating deployment with AWS Lambda and API Gateway
  1. AWS CloudWatch for model monitoring
  2. Optimizing model performance and cost
  3. Model retraining and A/B testing
  1.  Data security best practices on AWS
  2. AWS Identity and Access Management (IAM)
  3. Compliance requirements (GDPR, HIPAA)
  1. Building end-to-end ML pipelines using AWS Step Functions
  2. Automating workflows and scheduling tasks
  1. Designing, training, and deploying an ML model on AWS
  2. Evaluating and improving performance

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