The AI With AWS Online Training in Hyderabad - Visualpath Experts
                     Optimizing ML Model Performance on AWS
Strategies for Scalability, Efficiency, and Cost-Effectiveness
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Introduction
 Importance of ML model performance
 Common challenges (e.g., latency, cost, overfitting, 
scalability)
• Why AWS? (Scalability, managed services, 
integration)
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Model Development Optimization
 Choose the right ML algorithm and framework 
(e.g., XGBoost, PyTorch, TensorFlow)
 Use Amazon SageMaker for rapid prototyping 
and training
• Optimize data preprocessing and feature 
engineering with AWS Glue or SageMaker 
Pipelines
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Infrastructure Optimization
 Use appropriate instance types (CPU vs GPU vs 
Inferentia)
 Use Spot Instances and Managed Spot Training for 
cost reduction
• Leverage Auto Scaling and Elastic Inference for 
dynamic workloads
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Model Training Optimization
 Use distributed training with SageMaker 
Distributed or Horovod
 Perform hyperparameter tuning using 
SageMaker Automatic Model Tuning
• Reduce data transfer times with Amazon 
FSx or Pipe mode input
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Deployment Optimization
 Use SageMaker endpoints for real-time inference
 Batch Transform for offline predictions
• Multi-model endpoints for hosting multiple models 
on a single endpoint
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Monitoring and Performance Tuning
 Use Amazon Cloud Watch and 
SageMaker Model Monitor to track 
performance
 Set up alerts and automated retraining 
pipelines
 Collect inference metrics and analyze 
latency, throughput, and errors
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Cost Optimization Best Practices
 Use SageMaker Savings Plans or Serverless Inference
 Optimize storage with S3 Intelligent-Tiering
• Monitor and control costs with AWS Budgets and 
Cost Explorer
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Summary and Next Steps
 Recap key optimization areas: development, 
training, deployment, monitoring
 Start with low-hanging optimizations (instance 
types, batch processing)
 Explore advanced features: AutoML, MLOps 
pipelines, custom containers
• Resources: AWS documentation, whitepapers, 
workshops
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