Why enroll for AWS AI Practitioner Certification Course?
According to a survey conducted by Amazon, 92% of the organizations across geographies will start using AI-powered solutions by 2028.
AWS certification is rated as one of the most popular and lucrative cloud certifications in IT globally - Global Knowledge Study.
As per Glassdoor, the average annual salary for an AI engineer in the US is USD201,350, with a cash bonus of USD70,522.
AWS AI Practitioner Certification Training Benefits
According to AWS, companies are seeking AI-skilled employees in various domains and are willing to offer 43% higher salaries in sales and marketing, 42% higher in finance, 41% higher in business operations, and 47% higher in IT domains. This highlights the critical importance of receiving advanced AI training for professionals to stay competitive and position themselves for high-paying jobs.
Annual Salary
Hiring Companies
Want to become a Generative AI Engineer?
Annual Salary
Hiring Companies
Want to become a Generative AI Engineer?
Annual Salary
Hiring Companies
Want to become a Generative AI Engineer?
Why AWS AI Practitioner Certification Course from edureka
Live Interactive Learning
World-Class Instructors
Expert-Led Mentoring Sessions
Instant doubt clearing
Lifetime Access
Course Access Never Expires
Free Access to Future Updates
Unlimited Access to Course Content
24x7 Support
One-On-One Learning Assistance
Help Desk Support
Resolve Doubts in Real-time
Hands-On Project Based Learning
Industry-Relevant Projects
Course Demo Dataset & Files
Quizzes & Assignments
Industry Recognised Certification
Edureka Training Certificate
Graded Performance Certificate
Certificate of Completion
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About your AWS AI Practitioner Certification Course
AWS AI Practitioner Certification Course Skills Covered
Advanced AI Concepts
Design Considerations
Model Training and Tuning
Prompt Engineering
Responsible AI
Security Compliance
AWS AI Practitioner Certification Course Tools Covered
AWS AI Practitioner Certification Course Curriculum
Curriculum Designed by Experts
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Exploring Artificial Intelligence on AWS
Topics:
Introduction to Artificial Intelligence
Fundamentals of Machine Learning
Essential Concepts in Generative AI
Understanding Generative AI
Applications of Generative AI in Enterprises
Training Employees in Generative AI Usage
Techniques in Generative AI
Advantages of Using AWS for Generative AI Development
Overview of AWS Infrastructure and Technologies
AI and Machine Learning Services in AWS
Introduction to Amazon SageMaker
Hands-On:
Working with SageMaker
Working with SageMaker
Skills
Understanding Machine Learning and AI Concepts
Comprehend the principles and techniques of generative AI
Navigate and implement AWS infrastructure and technologies for AI/ML projects
AWS AI and ML Applications: Industry Use Cases
15 Topics
Topics:
AI applications across industries
AI-driven business solutions
Strategies for integrating AI into business processes
Evaluating limitations and constraints for AI adoption
Use cases for supervised learning in real-world applications
Applications and benefits of unsupervised learning techniques
Leveraging reinforcement learning for autonomous decision-making systems
Overview of generative AI technologies and their applications
Generative AI Industry use cases
Future trends and innovations enabled by generative AI
Ethical considerations in the development and deployment of generative AI
Factors to consider when selecting appropriate generative AI models
Methods for evaluating model performance and accuracy
Practical guidelines for choosing the right model for specific tasks
Evaluating the impact of generative AI on business outcomes
Hands-On:
AI Applications Across Industries
AI-Driven Business Solutions
Strategies for Integrating AI into Business Processes
AI, ML, and Generative AI Use Cases
Skills
Practical AI Applications
Industry-Specific AI Solutions
Generative AI Techniques
Machine Learning and Generative AI with AWS
18 Topics
Topics:
Machine Learning Lifecycle Management
Developing Machine Learning Solutions with Amazon SageMaker
Sources for Machine Learning Models
Evaluating the Performance of Machine Learning Models
Deploying Machine Learning Models
Essentials of MLOps
Lifecycle of Generative AI Applications
Addressing Bias in Generative AI Models
Introduction to Foundation Models in AWS
Importance of Foundation Models
Operational Mechanisms of Foundation Models
Types of Foundation Models
Design Considerations for Foundation Models
Selecting an Appropriate Foundation Model
Enhancing Foundation Model Performance
Evaluating Foundation Models
Developing Applications with Generative AI Foundation Models in AWS
Deploying Applications in AWS
Hands-On:
Building and Deploying ML Models with Amazon SageMaker
Evaluating ML Model Performance
Exploring Generative AI Foundation Models
Developing and Deploying Applications with Generative AI
Skills
Model Development and Deployment
Build and deploy generative AI applications
Generative AI with Amazon Bedrock and Amazon Q
10 Topics
Topics:
Amazon Bedrock Essentials
Amazon Bedrock Features
Working with Bedrock API
Building Generative AI Applications with Bedrock
Introduction to Amazon Q
Amazon Q Developer Basics
Amazon Q Architecture
Amazon Q Developer features
Working with Amazon Q Business
Generative BI with Amazon Q and Quicksight
Hands-On:
Building Generative AI Applications with Bedrock
Working with Amazon Q
Understanding Generative BI with Q and Quicksight
Skills
Building Applications with Bedrock
Leveraging Amazon Q
Generative BI with Q and Quicksight
Prompt Engineering for Generative AI Applications
10 Topics
Topics:
Understanding Prompts and Their Components
Techniques for Modifying Prompts
Parameters for Inference in Prompting
Best Practices in Prompting
Engineering Techniques for Effective Prompts
Zero-shot, Few-shot, and Chain-of-thought Prompting
Risks and Misuses of Prompts
Poisoning, hijacking, and prompt injection
Exposure and prompt leaking
Jailbreaking
Hands-On:
Designing and Modifying Prompts
Applying Prompt Engineering Techniques
Optimizing Inference Parameters
Identifying and Mitigating Prompt Misuses and Risks
Skills
Effective Prompt Design
Prompt Engineering Techniques
Risk Management in Prompt Engineering
Optimizing Foundation Models with RAG and Fine-Tuning
8 Topics
Topics:
Enhancing Foundation Models with Retrieval-Augmented Generation
Developing a Business Case
Introduction to Retrieval-Augmented Generation (RAG)
Utilizing Agents in RAG
Assessing and Analyzing Results
Improving Foundation Models through Fine-Tuning
Applying Fine-Tuning Techniques
Evaluating Model Performance
Hands-On:
Implementing Retrieval-Augmented Generation (RAG)
Optimizing Foundation Models with Fine-Tuning
Evaluating Model Performance
Skills
Implementing and Evaluating RAG
Fine-Tuning Foundation Model
Model Optimization Techniques
Advanced AWS Services for AI Application Development
Deploying Web Applications with AWS CloudFormation
Hands-On:
Reviewing Documents in S3 Buckets
Creating and Using Custom Classification Models
Deploying Large Language Models
Integrating Amazon Kendra and Lex V2
Querying and Implementing RAG Workflows
Deploying Web Apps with CloudFormation
Skills
Working with Amazon Comprehend
AI and Chatbot Development with Amazon Kendra and Lex V2
Advanced Model Querying and RAG Workflows
Deployment with AWS CloudFormation
Security Compliance and Governance with AWS
8 Topics
Topics:
AI System Compliance Standards
AWS Governance and Compliance Services
Data Governance Strategies
Implementing Governance Approaches
Security and Privacy in AI Systems
AWS Security Features for AI
Data and Model Lineage
Best Practices for Secure Data Engineering
Hands-On:
Implementing Data Governance and Compliance
Securing AI Systems
Skills
Governance and Compliance in AI Systems
AI Security and Policy
Responsible AI Practices and Principles (Self-paced)
9 Topics
Topics:
Understanding Responsible AI
Challenges in Implementing Responsible AI
Key Dimensions of Responsible AI
Amazon Tools and Services for Responsible AI
Considerations for Ethical Model Selection
Ethical Preparation of Datasets
Transparent and Explainable Models
Trade-offs in Responsible AI Models
Human-Centered Design Principles for Explainable AI
Hands-On:
Applying Amazon Services for Responsible AI
Skills
Responsible AI Implementation
Free Career Counselling
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About your AWS AI Practitioner Certification Course
What are the objectives of our AWS AI Practitioner Online Training?
Our AWS AI Practitioner Online Training aims to fully prepare you for the AWS Machine Learning - Associate (AIF-C01) test. During training, you will:
Recognize the fundamental ideas and terms used in machine learning (ML), generative AI, and artificial intelligence (AI).
Examine practical uses for generative AI, AI, and ML technologies that can spur innovation and address business issues.
Discover the design considerations and architectural ideas that go into creating strong foundation models for AI applications.
Learn the methods and strategies for fine-tuning machine learning models to get the performance you want.
Gain expertise in creating efficient questions and prompts that use generative AI models to produce targeted results.
Recognize the measures and standards used to assess the efficacy, fairness, and success of foundation models.
Learn about best practices and ethical issues for creating and using AI systems in a responsible manner.
Discover the safety and integrity of AI apps and data by learning about the necessary compliance standards and security procedures.
By the end of this course, you will be well-prepared to pass the AWS AWS AI Practitioner Foundational (AIF-C01) exam, having gained practical skills and knowledge that are relevant to the exam topics, ensuring readiness for real-world AWS artificial intelligence scenarios.
What are the prerequisites for Edureka's AWS AI Practitioner Certification Training?
There are no prerequisites for this course. However, participants will benefit from basic knowledge of Python programming, cloud computing, AWS fundamentals, and working knowledge of artificial intelligence.
What is the syllabus of the AWS AI Practitioner Certification Course?
We will cover the following topics to help you prepare for the AWS Machine Learning Engineer Associate (MLA - C01) examination and gain an overall understanding of the AI, ML, and Generative AI lifecycle and its implementation on AWS. The exam will cover the following topics:
Fundamental concepts and terminologies of AI, ML, and generative AI
Use cases of AI, ML and generative AI
Design considerations for foundation models
Model Training and fine tuning
Prompt engineering
Foundation model evaluation criteria
Responsible AI
Security and compliance for AI systems
Do I need coding knowledge to take up this program?
No, you do not need to have any prior coding experience to enroll in this course. We will provide the required materials as prerequisites so that you can begin your preparation before the live class starts. However, any prior coding experience will be an added advantage for the participants.
What is the duration of the AWS AI Practitioner Certification Course?
The duration of the AWS AWS AI Practitioner Certification Course is 24 hours.
What are the advantages of learning the AWS Certification Course from Edureka?
The AWS AWS AI Practitioner Foundational Course by Edureka helps you prepare for the certification exam (AIF-C01) and become a certified AI practitioner. You will have advantages like:
Access to instructor-led live sessions
Professional Community forum
24/7 expert support
Pre-requisite learning materials
Lifetime access to LMS, where presentations, quizzes, installation guides & class recordings are available
What are the system requirements for this AWS AI Practitioner Certification Course?
The system requirements to attend this AWS AI Practitioner Foundational Certification Course Online include a minimum of 8 GB RAM, an Intel Core i3 processor or above, 20 GB HDD, and a stable internet connection.
How will I execute practicals in this AWS AWS AI Practitioner Certification Course?
In this course, you will work on Amazon's cloud servers and other services. You can create a Free Tier account on AWS, giving you access to all the AWS services. The stepwise guide for accessing these services will be available in the LMS, and Edureka’s support team will assist you 24/7 if you have any doubts.
About your AWS AI Practitioner Certification Course Project
Enhanced Product Design using Generative AI on AWS
Designing products that match consumer aspirations for innovation and personalization while optimizing design processes is still a problem. Traditional design methodologies are g....
Email Spam Classification using Machine Learning on AWS
Email spam is an enormous issue, affecting user productivity and email security. Traditional rule-based email filtering solutions frequently fail to effectively distinguish betwe....
AWS AI Practitioner Certification Course Details
To unlock Edureka’s AWS AI Practitioner Certification Course completion certificate, you must ensure the following:
Completely participate in Edureka’s AWS AI Practitioner Certification Course.
Complete the quizzes and projects listed.
Cloud computing is in huge demand in the industry, and the latest studies suggest that the cloud computing market will continue to grow in the coming years. Various companies are switching from using their own data centers to third-party cloud service providers. Consequently, businesses have been able to reduce costs and improve service significantly. There will be a need for a workforce in the industry as the sector appears to grow considerably in the future.
Due to the rapid growth of artificial intelligence (AI) and its widespread use across various sectors, it offers promising career opportunities. The demand for skilled professionals in areas such as computer vision, natural language processing, and machine learning will increase as AI technologies advance. AI professions can offer significant financial rewards and make a meaningful difference in a range of sectors such as technology, healthcare, and finance. In order to thrive in this rapidly evolving field, budding AI professionals need to stay current with advancements in technology and acquire essential skills through training and hands-on experience.
Amazon Web Services (AWS) is Amazon's cloud computing platform. It provides several services, including computing, storage, networking, databases, artificial intelligence, machine learning, analytics, security, and more. AWS enables businesses to build and deploy applications and services with greater scalability and reliability compared to traditional on-premises infrastructure.
AWS is a user-friendly and popular cloud computing service that beginners can easily familiarize themselves with. Proper guidance and a well-structured training course are necessary to learn its services and functionality. Beginners aspiring to pursue a career in AWS can sign up for our training courses and earn certificates to demonstrate their expertise in this domain.
AWS certifications significantly boost your cloud computing knowledge and your ability to command a higher wage. AWS AI Practitioner Certification validates your skills and expertise in leveraging AWS for AI-powered solutions and positions you higher up in order to get Cloud-based AI and Generative AI-related jobs.
Amazon Web Services (AWS) has introduced multiple certifications across domains for professionals who want to upskill themselves as per their preferred learning path. These certificates are designed for professionals with different levels of experience and expertise in various domains, such as:
Associate: These certifications are role-based and allow aspirants to build their credibility as certified AWS professionals. Prior on-premise or cloud hands-on IT experience is required to clear the certification examination and obtain the certification.
Professional: These certifications are role-based and allow aspirants to build their credibility as certified AWS professionals. 2 years of AWS cloud experience are required to clear the certification examination and obtain the certification.
Specialty: These certifications are suitable for professionals who want to specialize in a particular field and position themselves as trusted advisors to their stakeholders and customers in these strategic areas.
The exam duration is 120 minutes.
The exam consists of 85 questions based on the topics outlined by AWS.
The exam costs 75 USD/10,000 JPY. Visit the Exam pricing page for additional cost information, including foreign exchange rates.
Individuals who have working knowledge of AI/ML technologies on AWS but may not actually develop solutions with them.
You can take the exam at a Pearson VUE testing location or online through a proctored exam.
The exam is offered in English and Japanese.
The World Economic Forum's Future of Jobs Report 2023 predicts a 40% increase in demand for AI and Machine Learning Specialists. However, 70% of IT leaders in North America report needing help recruiting qualified AI/ML personnel. Earning this certification can help you land sought-after artificial intelligence jobs in the AWS Cloud environment.
For those transitioning into the cloud industry, we suggest getting the AWS Certified Solutions Architect - Associate credential. We encourage anyone pursuing careers in data, artificial intelligence, and machine learning to consider obtaining certifications such as the AWS Certified Data Engineer - Associate or the AWS Certified Machine Learning Engineer - Associate.
No, we do not provide any exam vouchers for this course.
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AWS AI Practitioner Certification Course
What if I miss the AWS AI Practitioner Certification Course classes?
You will never miss a lecture at Edureka! as you can always view the recorded session of the class available in your LMS.
So, what are you waiting for?
Let’s enroll with Edureka and learn the best AWS AWS AI Practitioner Certification Course online with our top instructors.
Will I get placement assistance after completing this AWS AI Practitioner Certification Course?
To help you in this endeavor, we have added a resume builder tool to your LMS. Now, you will be able to create a winning resume in just three easy steps. You will have unlimited access to these templates across different roles and designations. You just need to log in to your LMS and click on the "create your resume" option.
Who are the instructors for the AWS AI Practitioner Certification Course?
All the instructors at Edureka are industry practitioners with a minimum of 10–12 years of relevant experience. They are subject matter experts trained by Edureka to provide an awesome learning experience to the participants.
What if I have more queries after completing the AWS AI Practitioner Certification Course?
Just give us a call at +91 98702 76459/1844 230 6365 (US Tollfree Number) or email at sales@edureka.co.
What if I miss a class of AWS AI Practitioner Certification Course?
You will never miss a lecture at Edureka! You can choose either of the two options:
View the recorded class session available in your LMS.
You can attend the missed session or any other live batch.
What if I have queries after I complete this AWS AI Practitioner Certification Course?
Your access to the Support Team is for a lifetime and will be available 24/7. The team will help you resolve queries during and after the completion of this certification course.
How soon after signing up would I get access to the Learning Content?
Post-enrolment, the LMS access will be instantly provided to you and will be available for a lifetime. You can access the complete set of previous class recordings, PPTs, PDFs, and assignments. Moreover, access to our 24×7 support team will be granted instantly. You can start learning right away.
Will the course material be available to learners after completion of the course?
Yes, once you enroll in the AWS AI Practitioner Certification Course, you will have lifetime access to the course material.
Is this course 100% online? Do I need to attend any physical classes?
This course is 100% online, and there will be no physical classes. This course can be accessed through the web on any device.