Cloud Wizard - Amazon SageMaker Studio for Data Scientist - 3 Days ILT

Accelerate your ML development lifecycle with this intensive, hands-on course delivered by Cloud Wizard Consulting - an AWS Advanced Training Partner and Select Tier Consulting Partner. Our authorized instructors hold all AWS certifications and have trained more than 5,000 professionals with a 100% passing rate.

Cloud Wizard's instructors bring deep, certified expertise in Amazon SageMaker and the broader AWS ML ecosystem. With small-class delivery, participants receive personalized guidance throughout every lab exercise. Our capstone project challenges mirror real-world scenarios - such as building a customer churn prediction pipeline on tabular transactional data - so you leave with skills immediately applicable to production workloads.

By the end of this course, participants will be able to accelerate the preparation, building, training, deployment, and monitoring of ML solutions for tabular data using Amazon SageMaker Studio's integrated toolset.

Experienced data scientists proficient in ML and deep learning fundamentals, with relevant experience using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models.

• AWS Technical Essentials
• Machine Learning Pipeline on AWS (3-day AWS Digital Classroom course)
• Deep Learning on AWS (1-day AWS instructor-led course)

Module 1: Amazon SageMaker Studio Setup - Configure JupyterLab extensions and navigate the SageMaker Studio interface so you can work efficiently from day one.

Module 2: Data Processing - Analyze and prepare data at scale using SageMaker Data Wrangler, Amazon EMR, AWS Glue interactive sessions, SageMaker Processing with custom scripts, and SageMaker Feature Store. Outcome: automate feature engineering workflows that reduce data prep time. Includes 4 hands-on labs.

Module 3: Model Development - Track experiments, train with built-in algorithms or custom containers, tune hyperparameters automatically, detect bias with SageMaker Clarify, and explore SageMaker JumpStart foundation models. Outcome: systematically iterate on models while maintaining full reproducibility. Includes multiple hands-on labs covering SageMaker Experiments, Debugger, and Clarify.

Module 4: Deployment and Inference - Register models, build CI/CD pipelines with SageMaker Pipelines, configure inference endpoints, and optimize performance with scaling and testing strategies. Outcome: deploy production-ready ML pipelines with governance controls. Includes hands-on labs.

Module 5: Monitoring - Implement Amazon SageMaker Model Monitor to detect data drift and model degradation in production. Includes case study discussion and live demonstration.

Module 6: Managing Resources and Capstone - Manage costs, shut down resources, and complete a capstone project with 7 challenges covering the full ML lifecycle on a realistic tabular dataset.

Lab environments are provisioned in isolated AWS accounts and are ephemeral - all participant data and artifacts are removed after the course concludes. No proprietary customer data is required for exercises; all labs use sample datasets provided within the training environment.

• 3 full days of live instructor-led training with real-time Q&A
• Hands-on labs with dedicated technical assistance
• Access to CodeWhisperer and CodeGuru Security scan extensions during labs
• Capstone project simulating end-to-end ML deployment
• Post-training guidance on AWS certification preparation

To book a session or schedule a free readiness consultation, visit https://cloudwizardconsulting.com/aws-training/amazon-sagemaker-studio-for-data-scientists/  or contact Cloud Wizard Consulting directly.