The Amazon SageMaker Studio for Data Scientists course is designed to provide learners with comprehensive training in using Amazon SageMaker Studio—an integrated development environment (IDE) for building, training, deploying, and monitoring machine learning (ML) models on AWS. This course equips data scientists with the skills needed to manage end-to-end ML workflows efficiently within the SageMaker Studio environment.

The course covers key areas such as:

• SageMaker Studio Setup and Navigation: Learn how to launch, configure, and navigate Amazon SageMaker Studio from the AWS console.
• Data Processing for ML: Explore techniques for cleaning, transforming, analyzing, and preparing data for machine learning models.
• Bias Detection and Baseline Models: Understand how to identify bias in datasets and create baseline model metrics.
• Model Development and Tuning: Develop and optimize machine learning models using built-in tools such as SageMaker Debugger and automatic hyperparameter tuning.
• Model Deployment and Inference: Learn how to deploy models into production using the SageMaker Model Registry and perform real-time or batch inference.
• Pipeline Automation: Use Amazon SageMaker Pipelines to build and orchestrate repeatable ML workflows.
• Monitoring and Drift Detection: Implement monitoring strategies to detect data drift, model performance issues, and ensure model quality over time.
• Resource Management: Manage resources and updates in SageMaker Studio, including cost optimization and environment configuration.

Course Prerequisites

To ensure success in this course, participants should meet the following prerequisites:

• Basic understanding of cloud computing and AWS services
• Familiarity with Amazon S3, Amazon EC2, and AWS IAM
• Foundational knowledge of data science workflows, including data preparation and visualization
• Proficiency in Python programming for data science and ML tasks
• Basic knowledge of machine learning principles and experience with ML models
• Ability to work in Linux-based environments and use command-line tools
• Comfort with using Jupyter Notebooks and development environments

These prerequisites help participants build on existing skills to confidently apply new concepts and tools taught in the course.

Target Audience

The Amazon SageMaker Studio for Data Scientists course is ideal for professionals looking to implement and manage machine learning workflows on AWS, including:

• Data Scientists
• Machine Learning Engineers
• AI/ML Researchers
• Data Analysts transitioning to predictive analytics
• Cloud Solutions Architects
• DevOps Engineers supporting ML pipelines
• IT Professionals managing cloud-based ML environments
• Business Intelligence Professionals expanding into ML
• Technical Product Managers overseeing ML-driven solutions
• Software Developers integrating ML into applications

Why Choose us

⭢ Live Online Training (Duration : 24 Hours)

⭢ Guaranteed to run classes

⭢ Experienced & certified trainers

⭢ Query Handling session

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Learning Objectives

After completing the Amazon SageMaker Studio for Data Scientists course, learners will be able to:

• Launch and navigate the Amazon SageMaker Studio environment from the AWS Console.
• Clean, transform, and analyze datasets to ensure ML-readiness.
• Build repeatable data workflows and validate data for machine learning applications.
• Detect and mitigate bias in training data and evaluate baseline model performance.
• Develop, tune, and evaluate machine learning models using SageMaker Studio.
• Perform automatic hyperparameter optimization for improved model performance.
• Use SageMaker Debugger to identify and resolve training issues.
• Manage and deploy models using the SageMaker Model Registry and apply them to inference use cases.
• Automate ML workflows using SageMaker Pipelines for efficient production deployment.
• Monitor model performance, detect drift, and maintain model quality.
• Manage Studio resources, control costs, and maintain an optimized ML environment.