Module 1
Understand core AI/ML terminology, supervised vs unsupervised learning, and typical machine learning workflows.
AWS Certified AI Practitioner
Learn to build intelligent, cloud-powered AI solutions on AWS. Master essential AI/ML concepts, services, and workflows to apply artificial intelligence in real-world business scenarios.
The AWS AI Practitioner certification is designed for professionals looking to understand and apply artificial intelligence and machine learning in AWS environments. This author-designed program blends AWS AI/ML services, hands-on labs, and real business use cases — helping you confidently build AI-enabled applications and workflows.
Who should join:
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Data Analysts & ML beginners
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Software Developers & Cloud Engineers
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Product & Business Managers exploring AI capabilities
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Anyone preparing for the AWS AI Practitioner certification
KEY FEATURES
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Author-designed training with real AI/ML use cases and hands-on AWS projects
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Covers essential AWS AI/ML tools: Amazon SageMaker, Bedrock, Rekognition, Comprehend, Lex
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16 hours of practical, applied learning
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End-to-end coverage of AI fundamentals, model lifecycle, and cloud integration patterns
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Hands-on labs with datasets, inference pipelines, and generative AI tools
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Includes exam preparation, practice questions, and certification guidance
Flexible delivery: Online Live | Classroom | Corporate groups
COURSE TRAINER
Rostyslav Myronenko,Solutions architect at Booking.com | CEO at Rost CAMP | AWS Community Builder | Consultant | Full AWS Certified
Solutions Architect at Booking.сom (Amsterdam, The Netherlands; from Kharkiv, Ukraine). Human-centric technical leader, manager, consultant and mentor with about 14 years of experience in different engineering roles. Main focus: solution architecture, AWS, Kubernetes, AI, SDLC, team leadership, mentoring, growing a team. AWS Community Builder in the AI Engineering category. Full AWS Certified (14 active AWS certifications), holder of AWS Golden Jacket.
COURSE PROGRAM
Module 2
Explore managed AI/ML tools: SageMaker, Bedrock, Rekognition, Comprehend, Polly, Lex, and more.
Module 3
Learn essential steps for preparing and processing data for AI-driven workflows.
Module 4
Create, train, deploy, and monitor basic models using SageMaker Studio and managed ML pipelines.
Module 5
Use Amazon Bedrock and foundation models to build generative AI applications and intelligent assistants.
Module 6
Integrate AI into real systems using API calls, event-driven architectures, and cloud-native services.
Module 7
Learn best practices for security, data privacy, bias mitigation, and ethical AI use.
Module 8
Optimize compute, training, and inference costs using AWS tools and pricing strategies.
Module 9
Practice with exam-style questions, hands-on labs, and real-life AI scenarios.
SCHEDULE
Next cohort:
June 9
Duration:
16 hours (4 weeks,
8 workshops× 2 hours)
Formats:
- Online Live
- Classroom in Tashkent
- Corporate Training on demand
PRICE
$1600
FAQ
Author-designed materials, AWS lab environments, datasets, exercises, and instructor support.
Online Live, Classroom in Tashkent, or Corporate formats — all interactive and instructor-led.
An AWS-certified AI/ML practitioner with real-world experience building and deploying AI solutions.
Yes. Access to recordings (where allowed) and materials is provided throughout the program.
The course includes hands-on labs, AI workflows, model deployment, and generative AI exercises.
Yes — live Q&A, lab reviews, and instructor feedback.
We offer corporate training, custom schedules, and onsite sessions for organizations in Uzbekistan.
The course is delivered in English, but language specifics can be discussed with the instructor for comfortable learning.