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Install and configure SageMaker Studio for an expert environment, create complete pipelines with processing jobs on massive datasets using Pandas and Scikit-learn, launch distributed training on GPU with TensorFlow or PyTorch on real enterprise cases, optimize hyperparameters via automatic tuning with detailed reports, perform practical exercises on your ML project, produce reproducible notebooks and receive personalized code review by the trainer for immediate skill enhancement.
Deploy scalable SageMaker endpoints with auto-scaling and VPC security, integrate bias detection via Clarify on real models, configure Model Monitor for real-time drift detection with CloudWatch alerts, implement CI/CD via CodePipeline for continuous deployments, test on enterprise scenarios with A/B testing, generate custom dashboards and cost optimization plans, conclude with an end-to-end project deployed in simulated production, highlighting your certifiable enterprise skills.
Target audience
Data scientists, ML engineers, AI architects seeking to upskill in enterprise MLOps
Prerequisites
Advanced Python expertise, ML frameworks (TensorFlow, PyTorch), AWS (S3, EC2), CI/CD pipelines
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