【UPDATED FOR MLA-C02】 Master Amazon SageMaker and Amazon Bedrock: Data Prep, Model Training, Deployment, and MLOps.
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Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
A workflow diagram illustrating the machine learning process, detailing data preparation, model creation, and evaluation stages with visual examples.
A JupyterLab notebook in SageMaker Studio displays Python code for splitting data into training and validation sets and uploading them to S3.
Course slide explaining ROC curves and AUC metrics for classification evaluation, featuring mathematical formulas and a plotted graph with shaded area.
A branded title card featuring the AWS Certified Machine Learning Engineer Associate badge and large text on a dark digital network background
Instruction extends beyond ML to include essential AWS services such as VPC networking, IAM security, and various database systems like RDS and DynamoDB.
The curriculum includes practical components for SageMaker tools, including JumpStart, Data Wrangler, and Feature Store.
The wide range of topics covering general cloud management and analytics may dilute the technical depth of specific machine learning workflows.
The curriculum aligns with the target audience of developers and data professionals preparing for the MLA-C01 exam.