Certified AI Practitioner on AWS
This one-day, foundational course validates knowledge of artificial intelligence (AI), machine learning (ML), and generative AI concepts and use cases. It is designed to sharpen your competitive edge and position you for career growth and higher earnings.b The course covers key concepts, methods, and strategies for AI, ML, and generative AI, both in general and on AWS. It also teaches you to identify appropriate technologies for specific use cases and to use these technologies responsibly.
Training at a glance
Level
Foundational
Duration
1 Day
Experience
Basic AI/ML
Average Salary
$85,866
Labs
Yes
Level
Beginner
Duration
4 Days
Experience
1 year: General IT
Average Salary
$85,866
Labs
Yes
Training Details
AWS Certified AI Practitioner validates in-demand knowledge of artificial intelligence (AI), machine learning (ML), and generative AI concepts and use cases. Sharpen your competitive edge and position yourself for career growth and higher earnings. The AWS Certified AI Practitioner (AIF-C01) exam is intended for individuals who can effectively demonstrate overall knowledge of AI/ML, generative AI technologies, and associated AWS services and tools, independent of a specific job role.
The exam also validates a candidate’s ability to complete the following tasks:
- Understand AI, ML, and generative AI concepts, methods, and strategies in general and on AWS.
- Understand the appropriate use of AI/ML and generative AI technologies to ask relevant questions within the candidate’s organization.
- Determine the correct types of AI/ML technologies to apply to specific use cases.
- Use AI, ML, and generative AI technologies responsibly.
Module 1: Machine Learning (ML) Fundamentals
Describe the ML development lifecycle.
Module 2: Fundamentals of Generative AI
Explain the basic concepts of generative AI.
Understand the capabilities and limitations of generative AI for solving business problems.
Describe AWS infrastructure and technologies for building generative AI applications.
Module 3: Applications of Foundation Models
Describe design considerations for applications that use foundation models.
Choose effective prompt engineering techniques.
Describe the training and fine-tuning process for foundation models.
Describe methods to evaluate foundation model performance.
Module 4: Guidelines for Responsible AI
Explain the development of AI systems that are responsible.
Recognize the importance of transparent and explainable models.
Module 5: Security, Compliance, and Governance for AI Solutions
Explain methods to secure AI systems.
Recognize governance and compliance regulations for AI systems.
The target candidate should have the following AWS knowledge:
- Familiarity with the core AWS services (for example, Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker) and AWS core services use cases
- Familiarity with the AWS shared responsibility model for security and compliance in the AWS Cloud
- Familiarity with AWS Identity and Access Management (IAM) for securing and controlling access to AWS resources
- Familiarity with the AWS global infrastructure, including the concepts of AWS Regions, Availability Zones, and edge locations
- Familiarity with AWS service pricing models
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