Learn to Build, Train & Deploy Machine Learning Models on AWS
What you will learn
Introduction to Amazon SageMaker: Explore the features and capabilities of SageMaker as a machine learning platform.
Introduction to Machine Learning: Understand the basics of machine learning, including supervised and unsupervised learning, algorithms, and models.
Data Visualization: Explore techniques for visualizing and understanding your data using tools and libraries available in SageMaker.
Model Training: Understand how to train machine learning models using SageMaker’s infrastructure, including distributed training and hyperparameter tuning.
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- Master the entire machine learning lifecycle, from initial data preparation to production deployment, all within a unified AWS environment.
- Gain hands-on experience with SageMaker Studio, an integrated development environment (IDE) specifically designed for machine learning workflows, enabling seamless experimentation and collaboration.
- Learn to leverage SageMaker Data Wrangler for efficient data preparation, including feature engineering and data cleaning, reducing the time spent on this critical pre-modeling phase.
- Explore advanced model development techniques such as utilizing pre-built SageMaker algorithms and custom model creation for tailored solutions.
- Understand the nuances of selecting and configuring appropriate compute instances for model training and inference to optimize performance and cost-effectiveness.
- Implement strategies for model evaluation and performance monitoring to ensure the accuracy and reliability of deployed machine learning solutions.
- Discover how to package and deploy your trained models as scalable, real-time inference endpoints or for batch processing using SageMaker’s deployment capabilities.
- Learn to integrate your SageMaker-developed models into existing applications and workflows, unlocking the power of ML for business insights and automation.
- Explore the concept of MLOps (Machine Learning Operations) and how SageMaker facilitates continuous integration, continuous delivery, and continuous training for machine learning models.
- Understand the importance of data drift detection and how to implement mechanisms to ensure your deployed models remain performant over time.
- Develop a practical understanding of building end-to-end ML projects that are both robust and scalable, preparing you for real-world industry challenges.
- Gain proficiency in using SageMaker Pipelines to orchestrate and automate complex ML workflows, ensuring reproducibility and efficiency.
- PROS:
- Provides a comprehensive, project-based approach to learning AWS SageMaker.
- Focuses on practical, real-world applications of machine learning on a leading cloud platform.
- Equips learners with the skills to build and deploy production-ready ML solutions.
- CONS:
- Requires a foundational understanding of cloud computing concepts and basic Python programming.
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