AWS AI Practitioner Certification Training

• Foundational knowledge of AWS services.
• Familiarity with basic machine learning and AI concepts.
• Basic understanding of data workflows and programming (Python is preferred but not mandatory).
• Hands-on experience with AWS services (optional but beneficial).

• Business Professionals, Data Analysts, Software Developers, AI Developers,Data Scientists, Machine Learning Engineers, Solutions Architects, Software Developers working on AI/ML solutions, Professionals seeking to validate their expertise in deploying machine learning on AWS.

Course Outline:

• Introduction to AI and AWS
• Understanding AI and Machine Learning
i. Definitions and key concepts
ii. Difference between AI, ML, and DL (Deep Learning)
• AWS AI/ML Overview
i. Role of AWS in AI and ML
ii. Core AWS services for AI
iii. Use cases and applications
• Certification Overview
i. Exam structure and format
ii. Key competencies and domains

• Machine Learning Basics
• Core Concepts of Machine Learning
i. Types of ML: Supervised, Unsupervised, Reinforcement Learning
ii. Steps in the ML pipeline.
• AWS Machine Learning Services
i. Amazon SageMaker overview
ii. Built-in algorithms in SageMaker
• Data Preparation for ML
i. Data cleaning, transformation, and feature engineering
ii. Tools for managing large datasets on AWS

• AWS AI Services Overview
• Vision Services
i. Amazon Rekognition: Image and video analysis
ii. Use cases: Facial recognition, object detection.
• Language Services
i. Amazon Comprehend: Natural Language Processing (NLP)
ii. Amazon Polly: Text-to-Speech
iii. Amazon Translate: Machine translation
• Conversational AI
i. Amazon Lex: Building chatbots
ii. Integration with other AWS services
• Prediction and Forecasting
i. Amazon Forecast: Time-series forecasting
ii. Amazon Personalize: Personalized recommendations

• Practical Implementation
• Hands-On with Amazon SageMaker
i. Setting up a SageMaker environment
ii. Building, training, and deploying a simple ML model
• Using Amazon Rekognition
i. Analyzing images and videos
ii. Setting up an image classification pipeline.
• Building Chatbots with Amazon Lex
i. Creating intents, slots, and utterances
ii. Integrating Lex with Lambda for advanced functionalities
• Implementing Forecasting
i. Setting up Amazon Forecast
ii. Analyzing forecast accuracy

• Security, Compliance, and Cost Management
• Securing AI Solutions
i. IAM roles and permissions
ii. Data encryption and compliance.
• Data Governance and Compliance
i. Managing metadata with Glue Data Catalog
ii. Ensuring compliance with regulatory requirements.
• Cost Optimization Techniques
i. Understanding AWS Free Tier for AI services
ii. Monitoring and managing costs with AWS Cost Explorer

• Capstone Project
• Project Development
i. Choose a real-world AI problem
ii. Use multiple AWS AI services for implementation.
• Demonstrate project
i. Q&A Session
ii. Project Presentation

• Certification Preparation
• Certification Tips
i. Time management and preparation strategies.
ii. Focus areas for success.
iii. Simulated exams for hands-on experience.
iv. Common mistakes and how to avoid them.
v. Key Topics and Objective for Certification exams.
• Q&A Session
i. Practice and Mock Tests
Training material provided: Yes (Digital format, including detailed notes, domain-specific study guides, and practice questions).
Any Additional Information

Participants will have access to AWS lab environments for hands-on practice. They are encouraged to have an active AWS account and basic knowledge of analytics workflows. Setup instructions will be provided before the course.
This course ensures participants are prepared for the certification exam and equips them with the practical skills needed for real-world data analytics solutions on AWS.

Certification Code:
There isn’t a specific "AI Practitioner" certification from AWS. If you’re referring to the AWS Certified Machine Learning – Specialty certification, the exam code is MLS-C01. For general AI concepts, AWS provides foundational training rather than a standalone AI Practitioner certification.

Cost:
• Exam Fee: $300 (subject to change).
• Practice Exam Fee: $40.

Exam Format:
• Type of Questions: Multiple-choice and multiple-response.
• Number of Questions: 65.
• Time Duration: 170 minutes.
• Delivery Method:
o Online proctored exam.
o In-person exam at AWS-authorized testing centers.

Languages Available:
• English, Japanese, Korean, and Simplified Chinese.

Recommended Preparation Resources:
1. AWS Training and Certification Portal:
o Courses like Machine Learning Basics, Introduction to Amazon SageMaker, and Deep Learning on AWS.
2. AWS Whitepapers and Documentation:
o Read AWS Machine Learning Lens for the Well-Architected Framework.
o Review service-specific guides for SageMaker, Rekognition, Polly, and Comprehend.
3. Books and Guides:
o Hands-On Machine Learning with AWS by Joshua Arvin Lat.
o Data Science on AWS by Chris Fregly and Antje Barth.
4. Hands-On Practice:
o Experiment with AWS AI services using free-tier access.
o Build real-world projects such as image classification, text sentiment analysis, or chatbot creation.
5. Practice Exams:
o Official practice tests from AWS.
o Third-party providers like Whizlabs and A Cloud Guru.
Exam Scoring:
• Passing Score: 750 out of 1,000.
• Scaled scoring ensures fairness across test versions.
Career Benefits:
• Positions you as a valuable resource for organizations seeking to implement AI/ML solutions.
• Opens doors to roles such as ML Engineer, Data Scientist, and AI Solutions Architect.
• Enhances credibility for businesses leveraging AWS AI services for transformation projects.
Renewal:
AWS certifications are valid for three years. To maintain certification, you must retake the exam or earn a higher-level AWS certification in the same domain before it expires.
If you have a specific AWS service or AI-related certification in mind, feel free to clarify so I can provide tailored guidance!