Cloud Wizard - Practical Data Science with Amazon SageMaker - 1 Day ILT

Delivered by Cloud Wizard Consulting, an AWS Advanced Training Partner and Select Tier Consulting Partner with over 5,000 professionals trained. Our authorized instructors hold all AWS certifications and maintain a 100% passing rate, ensuring you learn from practitioners with deep, validated expertise.

In this intermediate-level, 1-day instructor-led course, your DevOps engineers and application developers will experience the full lifecycle of a data science project on AWS. Rather than just learning theory, participants build, train, evaluate, tune, and deploy a working ML model using Amazon SageMaker - leaving with the skills to independently ship ML-powered features and collaborate effectively with data science teams.

This course is designed for Development Operations (DevOps) engineers and application developers who need to integrate machine learning into production applications. After completing this training, participants can independently deploy a SageMaker endpoint and generate predictions - reducing reliance on specialized data science hires for common ML tasks.

• After Module 1 (Introduction to Machine Learning): Frame business problems as ML problems and identify which ML approach fits your use case - whether retail demand forecasting, healthcare risk scoring, or fintech fraud detection.
• After Module 2 (Preparing a Dataset): Use SageMaker Data Wrangler to clean, transform, and engineer features from raw data, ready for model training.
• After Module 3 (Training a Model): Train ML models in Amazon SageMaker and leverage Amazon CodeWhisperer in SageMaker Studio Notebooks to accelerate development.
• After Module 4 (Evaluating and Tuning a Model): Run hyperparameter optimization jobs to systematically improve model performance.
• After Module 5 (Deploying a Model): Deploy a trained model to a real-time SageMaker endpoint and generate live predictions.
• After Module 6 (Operational Challenges): Plan for responsible ML, automated retraining, monitoring, and model updates in production.
• After Module 7 (Other Model-Building Tools): Integrate a web application with a SageMaker model endpoint and explore no-code ML with SageMaker Canvas.

Participants complete multiple hands-on labs throughout the day, building a complete ML workflow from data preparation through deployment. Labs use isolated AWS environments provisioned specifically for the training session.

Throughout the course, participants work through a realistic business scenario - for example, building a predictive model that could apply to retail demand forecasting or customer churn prediction. The workflow covers data ingestion, feature engineering, model training, evaluation, and deployment to a live endpoint, mirroring what teams do in production.

Lab environments are provisioned as isolated AWS accounts dedicated to the training session. Participant data and enrollment information are handled in accordance with AWS security best practices. Lab environments are decommissioned after the course concludes.

• AWS Technical Essentials (or equivalent experience)
• Entry-level knowledge of Python programming
• Entry-level knowledge of statistics

• AWS Advanced Training Partner and Select Tier Consulting Partner
• 5,000+ professionals trained across organizations of all sizes
• All instructors hold every AWS certification - learn from validated experts
• 100% passing rate - every participant completes the course successfully
• Post-training guidance on applying best practices and certification preparation

Contact Cloud Wizard Consulting to discuss available dates, group enrollment options, and how this training accelerates your team's ML capabilities.