AWS Certified AI Practitioner Certification Training Course

Course Overview

The AWS Certified AI Practitioner Foundational Certification Training Course offered by Accumentum provides a comprehensive introduction to artificial intelligence (AI), machine learning (ML), and generative AI concepts specifically tailored for AWS services. This foundational course is designed for individuals who work with AI technologies but do not necessarily develop AI/ML solutions, such as business analysts, IT managers, and sales professionals. It covers essential topics like fundamental AI concepts, practical use cases, and the ethical implementation of AI through AWS services like Amazon SageMaker and Amazon Bedrock. Participants will explore the AWS AI ecosystem, learning how to identify opportunities for AI applications, understand the design considerations for foundation models, and recognize the importance of responsible AI practices. The course also includes modules on security, compliance, and governance for AI solutions, ensuring learners are well-prepared for the AWS Certified AI Practitioner (AIF-C01) exam. By the end of the training, participants will possess the knowledge to leverage AI for business innovation and understand the impact of AI on organizational strategies.

Course Objectives

  • Gain a solid grasp of artificial intelligence concepts, including machine learning, deep learning, and generative AI, tailored to AWS platforms.
  • Learn to recognize and evaluate opportunities where AI can drive business value, understanding how to align AI solutions with organizational goals using AWS services.
  • Explore the ethical considerations and responsible practices in AI development, focusing on bias mitigation, data privacy, and compliance with AWS tools.
  • Equip learners with the necessary knowledge and skills to pass the AWS Certified AI Practitioner (AIF-C01) exam, covering exam topics like AI service selection, model lifecycle management, and AI security.

Who Should Attend

  • Professionals who need to understand how AI can be integrated into business processes to drive insights and decision-making.
  • Individuals responsible for planning and overseeing projects involving AI technologies, ensuring alignment with business strategies and AWS best practices.
  • Those who wish to effectively communicate and sell AI solutions, or understand how AI can enhance marketing strategies using AWS services.
  • Anyone in technical roles who interact with AI but do not directly build solutions, such as support staff, product managers, or consultants looking to expand their knowledge of AWS AI capabilities.

Prerequisites

  • Familiarity with general IT concepts, including cloud computing, is recommended, though not mandatory.
  • This course is designed for beginners in AI; no previous experience with machine learning or AI is necessary.
  • A basic understanding of AWS services can be beneficial, but the course will cover AWS-specific AI tools in detail.
  • An eagerness to learn about artificial intelligence, machine learning, and their business applications is essential.

Course Content

Introduction to AI and AWS
  • Understanding what constitutes AI and Machine Learning.
  • Overview of AWS services related to AI.
  • How AI is transforming industries.
  • Key concepts and terms used in AI discussions.
Fundamentals of Machine Learning
  • Supervised vs. Unsupervised Learning: Basic differences and use cases.
  • Model Training and Deployment: Lifecycle of a machine learning model.
  • Feature Engineering: Importance of data preparation for ML.
  • AWS SageMaker Basics: Introduction to AWS’s primary ML service.
Generative AI and AWS
  • What is Generative AI?: Understanding generative models.
  • Amazon Bedrock: AWS’s platform for customizing foundation models.
  • Use Cases: Real-world applications of generative AI on AWS.
  • Ethical Considerations: Challenges and ethical implications of generative AI.
AWS AI Services Overview
  • Amazon Rekognition: Image and video analysis capabilities.
  • Amazon Lex: Building conversational interfaces.
  • Amazon Polly: Text-to-speech conversion.
  • Amazon Forecast: Time-series forecasting.
Data Handling for AI
  • Data Collection: Methods and best practices for gathering data.
  • Data Storage: AWS options for storing data for AI/ML.
  • Data Quality and Governance: Ensuring data integrity for AI use.
  • Data Privacy in AI: Compliance and security considerations.
AI Solution Design
  • Solution Architecture: Designing AI solutions on AWS.
  • Cost Optimization: Considerations for cost-effective AI solutions.
  • Scalability: Ensuring AI applications can scale with demand.
  • Integration with Existing Systems: How to incorporate AI into current tech stacks.
Responsible AI Practices
  • Bias and Fairness: Identifying and mitigating bias in AI models.
  • Transparency: Building trust through explainable AI.
  • AI Ethics: Frameworks for ethical AI deployment.
  • AWS Tools for Responsible AI: Services to aid ethical AI practices.
Security in AI on AWS
  • Data Protection: Techniques to secure data in AI applications.
  • Model Security: Protecting ML models from attacks.
  • Compliance: Meeting regulatory requirements for AI.
  • AWS Security Services: Tools like AWS Shield, WAF for AI security.
Case Studies and Use Cases
  • Industry-Specific Examples: AI applications in various sectors.
  • Success Stories: Real-world implementations of AI on AWS.
  • Lessons Learned: Common pitfalls and how to avoid them.
  • Innovation with AI: Encouraging creative solutions using AWS AI tools.
Preparing for the AWS AI Practitioner Exam
  • Exam Format: What to expect from the certification exam.
  • Study Resources: Recommended materials and AWS tools for preparation.
  • Practice Questions: Sample questions to test knowledge.
  • Understanding how this certification fits into broader AWS certification pathways.

Course Features

Interactive Learning

Engage with expert instructors and peers through training sessions, discussions, and practical exercises.

Comprehensive Study Materials

Access extensive resources, including e-books, video lectures, and practice exams.

Real-World Applications

Work on real-life case studies and scenarios to apply AWS AI and ML concepts.

Certification Preparation

Receive guidance and tips to successfully pass the AWS Certified AI Practitioner foundational certification exam.

Certification Exam

Upon completing the AWS Certified AI Practitioner Certification Training Course with Accumentum, you will be comprehensively prepared to sit for the AWS Certified AI Practitioner exam. This credential validates your foundational understanding of AI concepts and AWS AI services, showcasing your ability to identify AI opportunities, understand ethical AI practices, and utilize AWS tools for AI application. Earning the AWS Certified AI Practitioner certification will significantly boost your career trajectory, positioning you for roles that require strategic AI knowledge and leadership in leveraging AI technologies within an AWS environment.

Enrollment

Enroll in the AWS Certified AI Practitioner Certification Training Course with Accumentum to elevate your AI knowledge to a foundational level and earn a distinguished credential. This course is your route to becoming a certified AI practitioner on AWS. For detailed information and to secure your spot, visit Accumentum’s registration page linked below.