🧩 Part 8/8

✔ Summary of the AWS AutoML Project
✔ Evaluation of the Machine Learning Model
✔ Performance Metrics and Confusion Matrix
✔ Feature Importance and Partial Dependence Plots
✔ Recommendations for Future Work
✔ Data Collection and Preprocessing
✔ Model Selection and Tuning
✔ Deployment and Monitoring
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🧩 Part 7/8

✔ Model Optimization with AWS AutoML
✔ Hyperparameter Tuning
✔ Feature Engineering
✔ Model Automation with AWS Step Functions
✔ Creating a State Machine
✔ Integrating AWS AutoML Services
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🧩 Part 6/8

✔ Why Model Monitoring and Maintenance is Important
✔ How AWS AutoML Supports Model Monitoring and Maintenance
✔ How to Set Up AWS SageMaker Model Monitor
✔ How to Monitor Model Performance and Detect Model Drift
✔ How to Update or Retrain Your Model Using AWS AutoML
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🧩 Part 5/8

✔ Creating a Model Endpoint
✔ Invoking the Model Endpoint
✔ Invoking from AWS Console
✔ Invoking from AWS CLI
✔ Invoking from Python SDK
✔ Monitoring the Model Endpoint
✔ Viewing Metrics and Logs
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🧩 Part 4/8

✔ Model Evaluation
✔ Model Metrics
✔ Model Performance
✔ Model Interpretation
✔ Feature Importance
✔ SHAP Values
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🧩 Part 3/8

✔ Overview of Model Selection and Training in AWS AutoML
✔ How to Create an AutoML Job
✔ Specify the Data Source and Target Attribute
✔ Choose the Problem Type and Objective Metric
✔ Configure the AutoML Job Settings
✔ How to Monitor the AutoML Job Progress
✔ How to Compare and Select the Best Candidate Model
✔ How to Evaluate the Model Performance on Test Data
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🧩 Part 2/8

✔ Data Preparation with AWS Data Wrangler
✔ Install and Import AWS Data Wrangler
✔ Load Data from Amazon S3
✔ Clean and Transform Data
✔ Save Data to Amazon S3
✔ Data Analysis with AWS Glue
✔ Create a Glue Crawler
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🧩 Part 1/8

✔ What is AWS AutoML?
✔ Why use AWS AutoML?
✔ Benefits of AWS AutoML
✔ Use cases of AWS AutoML
✔ How to get started with AWS AutoML?
✔ Create an AWS account
✔ Configure your AWS environment
✔ Access the AWS AutoML console
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