This course provides you with a clear and structured understanding of the fundamental concepts of Artificial Intelligence (AI) and Machine Learning (ML), using Amazon Web Services (AWS) tools and services. Designed for professionals with no prior programming or data science experience, the program prepares you to obtain the official AWS Certified AI Practitioner (AIF-C01) certification.
Throughout the course, you will learn to identify real-world AI use cases, distinguish between different types of learning (supervised, unsupervised, and reinforcement learning), and understand how generative language models such as Foundation Models work. You will also explore key AWS services such as SageMaker JumpStart, Amazon Bedrock, Amazon Q, and PartyRock, applying governance and ethical principles to the responsible use of AI.
Upon completing this certification, the student will be able to:
- Understand essential concepts of AI, ML, and generative models
- Identify and assess the most common AI use cases in business environments
- Apply pretrained models and AI services available on AWS
- Implement best practices for the ethical and responsible use of artificial intelligence
- Recognize key aspects of security, privacy, and governance in AI projects
No prior knowledge is strictly required, but it is highly recommended that the candidate:
- Has a general understanding of cloud computing
- Understands basic data and programming concepts
- Is familiar with the AWS environment (console, IAM, basic services)
- Purpose of these prerequisites:
- To ensure a smooth learning curve focused on the practical application of AI models, without getting bogged down in overly technical fundamentals
| Certificación internacional AWS Certified AI Practitioner (AIF‑C01) | Applies |
|---|---|
| Certificación internacional AWS Certified AI Practitioner (AIF‑C01) | 24 hours |
Learning Methodology
The learning methodology, regardless of the modality (in-person or remote), is based on the development of workshops or labs that lead to the construction of a project, emulating real activities in a company.
The instructor (live), a professional with extensive experience in work environments related to the topics covered, acts as a workshop leader, guiding students' practice through knowledge transfer processes, applying the concepts of the proposed syllabus to the project.
The methodology seeks that the student does not memorize, but rather understands the concepts and how they are applied in a work environment.
As a result of this work, at the end of the training the student will have gained real experience, will be prepared for work and to pass an interview, a technical test, and/or achieve higher scores on international certification exams.
Conditions to guarantee successful results:
- a. An institution that requires the application of the model through organization, logistics, and strict control over the activities to be carried out by the participants in each training session.
- b. An instructor located anywhere in the world, who has the required in-depth knowledge, expertise, experience, and outstanding values, ensuring a very high-level knowledge transfer.
- c. A committed student, with the space, time, and attention required by the training process, and the willingness to focus on understanding how concepts are applied in a work environment, and not memorizing concepts just to take an exam.
Pre-enrollment
You do not need to pay to pre-enroll. By pre-enrolling, you reserve a spot in the group for this course or program. Our team will contact you to complete your enrollment.
Pre-enroll nowInfinity Payments
Make your payment quickly, safely and reliably
- For bank transfer payments, request the details by email
capacita@aulamatriz.edu.co.
- If you wish to finance your payment through our credit options
(Sufi, Cooperativa Unimos or Fincomercio), click on the following link:
Ver opciones de crédito.
Description
This course provides you with a clear and structured understanding of the fundamental concepts of Artificial Intelligence (AI) and Machine Learning (ML), using Amazon Web Services (AWS) tools and services. Designed for professionals with no prior programming or data science experience, the program prepares you to obtain the official AWS Certified AI Practitioner (AIF-C01) certification.
Throughout the course, you will learn to identify real-world AI use cases, distinguish between different types of learning (supervised, unsupervised, and reinforcement learning), and understand how generative language models such as Foundation Models work. You will also explore key AWS services such as SageMaker JumpStart, Amazon Bedrock, Amazon Q, and PartyRock, applying governance and ethical principles to the responsible use of AI.
Objectives
Upon completing this certification, the student will be able to:
- Understand essential concepts of AI, ML, and generative models
- Identify and assess the most common AI use cases in business environments
- Apply pretrained models and AI services available on AWS
- Implement best practices for the ethical and responsible use of artificial intelligence
- Recognize key aspects of security, privacy, and governance in AI projects
No prior knowledge is strictly required, but it is highly recommended that the candidate:
- Has a general understanding of cloud computing
- Understands basic data and programming concepts
- Is familiar with the AWS environment (console, IAM, basic services)
- Purpose of these prerequisites:
- To ensure a smooth learning curve focused on the practical application of AI models, without getting bogged down in overly technical fundamentals
offers
| Certificación internacional AWS Certified AI Practitioner (AIF‑C01) | Applies |
|---|---|
| Certificación internacional AWS Certified AI Practitioner (AIF‑C01) | 24 hours |
Learning Methodology
The learning methodology, regardless of the modality (in-person or remote), is based on the development of workshops or labs that lead to the construction of a project, emulating real activities in a company.
The instructor(live), a professional with extensive experience in work environments related to the topics covered, acts as a workshop leader, guiding students' practice through knowledge transfer processes, applying the concepts of the proposed syllabus to the project.
La metodología persigue que el estudiante "does not memorize", but rather "understands" the concepts and how they are applied in a work environment."
As a result of this work, at the end of the training the student will have gained real experience, will be prepared for work and to pass an interview, a technical test, and/or achieve higher scores on international certification exams.
Conditions to guarantee successful results:
- a. An institution that requires the application of the model through organization, logistics, and strict control over the activities to be carried out by the participants in each training session.
- b. An instructor located anywhere in the world, who has the required in-depth knowledge, expertise, experience, and outstanding values, ensuring a very high-level knowledge transfer.
- c. A committed student, with the space, time, and attention required by the training process, and the willingness to focus on understanding how concepts are applied in a work environment, and not memorizing concepts just to take an exam.
Infinity Payments
Make your payment quickly, safely and reliably
- For bank transfer payments, request the details by email
capacita@aulamatriz.edu.co.
- If you wish to finance your payment through our credit options
(Sufi, Cooperativa Unimos or Fincomercio), click on the following link:
Ver opciones de crédito.
Course Modules
Module I: Fundamentals of AI and ML
Introduction to key concepts such as AI, ML, deep learning, neural networks, and NLP. Study of types of inference (batch vs real-time), data classification (structured, unstructured), and differences between supervised, unsupervised, and reinforcement learning.
Module II: Fundamentals of Generative AI
Coverage of generative models: tokens, embeddings, prompt engineering. Exploration of use in text generation, images, audio, summarization, chatbots, and code. Lifecycle of a model (pre-training to deployment)
Module 3: Applications of Foundation Models
Designing solutions using foundation models: selection criteria (cost, latency, language), use of RAG, vector databases (OpenSearch, Aurora, Neptune), fine-tuning techniques, chain-of-thought prompting, and evaluation with metrics (BLEU, ROUGE, BERTScore)
Module IV: Principles of Responsible AI
Implementation of ethical AI: fairness, inclusion, transparency, and explainability. Use of tools such as SageMaker Clarify, Model Cards, guardrails in Bedrock. Coverage of biases, environmental impact, and legal implications
Module V: Security, Compliance, and Governance
Security management in AI solutions: IAM, encryption, Macie, PrivateLink; shared responsibility model. Governance strategies, auditing, compliance (ISO, SOC), AWS Config and Inspector
Module VI: Services and Architectures in AWS for AI
Practical review of relevant AWS tools: SageMaker, Bedrock, Comprehend, Rekognition, Lex, Polly, Translate; underlying infrastructures (S3, EC2, Lambda, VPC)
Module VII: Guided Practical Labs
Hands-on development: simple ML pipelines, Q&A on prompt tuning, use of Bedrock, and model evaluation with SageMaker tools.
Module VIII: Practice Tests and Exam Techniques
Official format simulations (multiple-choice, matching, ordering, case study), flashcards, and review of question patterns
Module IX: Business Use Cases
Analysis of real scenarios in finance, health, marketing, support: selection of models, data flow, risks and mitigation.
Module X: Study of Operational Architecture
Integration of AI in business processes: security in AI flows, monitoring, cost efficiency, scalability.
Module XI: Final Preparation and Review
Summary of best practices, resources (Well-Architected, whitepapers, exam guide), personalized study plan, and pre-exam checklist.