Practical Data Science with Amazon SageMaker Course
- Duration: 8 hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The Practical Data Science with Amazon SageMaker Course is designed to help organizations build practical, end-to-end data science capabilities using AWS-managed machine learning services. This course focuses on designing, building, training, tuning, and deploying machine learning models using Amazon SageMaker. With a structured and governance-aligned approach, the program enables enterprise teams to operationalize data science workflows efficiently while ensuring scalability, security, and consistency across projects.
Course Details
- Establishes standardized data science workflows using Amazon SageMaker
- Improves efficiency in model development, training, and deployment
- Aligns machine learning practices with globally recognized AWS frameworks
- Ensures consistent learning delivery across enterprise teams
- Enhances productivity through hands-on data science use cases
- Supports structured readiness for enterprise ML adoption and enablement
- Open to corporate professionals and data science teams
- Suitable for foundational to intermediate participants
- Recommended for teams working with data analytics and machine learning solutions
- Basic understanding of data analysis and Python programming preferred
- Familiarity with cloud computing concepts is beneficial
- Corporate sponsorship or group participation encouraged
- Access to a digital learning platform required
- Build and manage machine learning workflows using Amazon SageMaker
- Prepare, train, tune, and deploy ML models at scale
- Apply best practices for model monitoring and lifecycle management
- Gain hands-on experience through structured practical modules
- Improve collaboration between data science and engineering teams
- Align machine learning initiatives with organizational business objectives
- Enable reliable and scalable ML solutions in production environments
- Introduction to data science and machine learning on AWS
- Overview of Amazon SageMaker components and architecture
- Data preparation and feature engineering
- Model training, tuning, and evaluation
- Deploying and managing models using SageMaker endpoints
- Monitoring, scaling, and optimizing ML workloads
- Security, governance, and cost management for ML solutions
- Practical labs and real-world data science scenarios
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