In this machine learning course, you will learn about the machine learning lifecycle and how to use AWS services at every stage. Additionally, you will discover the diverse sources of machine learning models and learn techniques for evaluating their performance. You will also understand the importance of machine learning operations (MLOps) to speed the development and deployment of your machine learning projects.
Course level: Fundamental
Duration: 1 hour
Activities
This course includes interactive elements, text instructions, illustrative graphics, and knowledge checks.
Course objectives
In this course, you will learn how to do the following:
Describe the components of the machine learning lifecycle.
Identify relevant AWS services and resources for each stage of the ML lifecycle.
Explain the types of data used to train artificial intelligence (AI) models.
Understand the sources of machine learning models.
Understand model performance metrics.
Describe methods for using a model in production.
Understand the fundamental concepts of MLOps.
This course is aimed at:
Individuals interested in machine learning and artificial intelligence, regardless of a specific role
prerequisites
Developing machine learning solutions is part of a series that facilitates a foundation in artificial intelligence, machine learning, and generative AI. If you haven't already done so, it is recommended that you complete these two courses:
Fundamentals of Machine Learning and Artificial Intelligence
Exploring artificial intelligence use cases and applications
Course Outline
Section 1:
Lesson 1: How to use this course
Section 2:
Lesson 2: Introduction
Section 3: ML Solution Development
Lesson 3: Machine Learning Development Lifecycle
Lesson 4: Developing ML Solutions with Amazon SageMaker
Lesson 5: ML Model Sources
Lesson 6: Performance evaluation of machine learning models
Lesson 7: Model Deployment
Lesson 8: MLOps Fundamentals
Lesson 9: Knowledge Check
Section 4: Conclusion
Lesson 10: Resources
Lesson 11: Conclusion
Lesson 12: Contact us
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