Exam AWS MLS Machine Learning

[MLS-C01] [IMPL and OPs] Deploy and Operationalize

Deploy and operationalize machine learning solutions with lab

Posted by Oscaner on August 8, 2022

Lifecycle

Create your data
- Gather, engineer, transform
Produce your model
- Build model and evaluate it
Execute (operationalize) your model
- Deploy, monitor, retrain

Deployment and Operationalization

Deploy
Inference
Metrics
Monitor performance
Analysis
Retrain

Real-Time

Batch

Model Monitor

Monitor your production model to detect deviations in data quality compared to a baseline dataset

Monitor your endpoint using Model Monitor (csv or flat-json)
1. Baseline
2. SageMaker suggests baseline constraints
3. Create baselining job
4. Create a continuous monitoring schedule
5. Start continuous monitoring
Analyze / Retrain

Performance

To optimize the performance of your models you can use Automatic Model Tuning to find the hyperparameters that will give you the optimal performance
You can use SageMaker hosting to automatically scale your model to the performance needed for your model

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