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5 AWS Projects to Become an AI/ML Engineer

0h 0m 0s
English
Paid

5 AWS Projects to Become an AI/ML Engineer is a self-paced course by Tech with Lucy (Lucy Wang). Unlock your potential as an AI/ML Engineer with five hands-on projects on AWS.

Course facts

Lessons
0
Duration
self-paced
Level
All levels
Language
English
Updated
Instructor
Tech with Lucy (Lucy Wang)
Price
Premium

Unlock your potential as an AI/ML Engineer with five hands-on projects on AWS. This course is designed to offer you practical experience, using modern AI APIs to solve real industrial scenarios. From chatbots with document access to generative AI assistants, you will learn to design, deploy, and integrate AI systems in a cloud environment.

Course Overview

Throughout this course, you will engage in the development of event-driven serverless architectures, master sentiment analysis and natural language processing (NLP), explore Retrieval-Augmented Generation (RAG), and gain proficiency in training and deploying machine learning models. Additionally, you will delve into computer vision and create generative AI applications with an emphasis on security, logging, and monitoring in a production setting.

Projects Included

1. Smart Serverless Inbox with Sentiment Analysis

Develop a serverless architecture to perform real-time sentiment analysis of emails, ensuring efficient message handling and priority assignment.

2. Intelligent FAQ Chatbot using RAG

Create a chatbot that utilizes Retrieval-Augmented Generation to provide accurate and contextually informed responses by accessing vast document databases.

3. Subscription Prediction Model with SageMaker

Build a predictive model to analyze subscription trends using Amazon SageMaker, tailored to refine marketing strategies and business decisions.

4. Emotion Recognition Service through Hugging Face

Implement a computer vision service that detects emotions from images using Hugging Face, enhancing customer engagement and experience.

5. Cloud AI Tutor with Generative Model

Craft a secure AI tutor capable of providing guided learning experiences with a generative model backend, offering a personalized education platform.

Learning Outcomes

By completing this course, you will gain not only a robust theoretical understanding of AI/ML but also practical expertise by finishing five comprehensive projects for your portfolio. These projects illustrate your proficiency in sentiment analysis, RAG, ML deployment, computer vision, and generative AI within real AWS environments—key skills demanded from an entry-level AI/ML engineer.

Additional

  • This is a text-based course. Please download the archive to get started.

Who teaches 5 AWS Projects to Become an AI/ML Engineer? Tech with Lucy (Lucy Wang)

Tech with Lucy (Lucy Wang) thumbnail

Lucy Wang publishes the Tech with Lucy brand — focused on the AWS cloud-engineering career path and the AI/ML-on-AWS stack. Her teaching emphasises building real working portfolio projects rather than studying for certification exams as a goal in themselves.

Her CourseFlix listing carries two Tech with Lucy courses: 5 AWS Projects to Become a Cloud Engineer and 5 AWS Projects to Become an AI/ML Engineer. The project-based format suits self-taught engineers building portfolio work that demonstrates real AWS competence to potential employers.

Material is paid and aimed at engineers building AWS career portfolio work. For broader content, see CourseFlix's AWS and AI App Building category pages.

What courses are similar to 5 AWS Projects to Become an AI/ML Engineer?

Frequently asked questions

What prerequisites are needed before enrolling in this course?
Prospective students should have a basic understanding of cloud computing concepts and familiarity with AWS services. Knowledge of machine learning fundamentals and programming skills in Python will be beneficial, as the course involves developing and deploying models using Amazon SageMaker and other AI tools.
What projects will I work on during the course?
The course includes five hands-on projects: developing a Smart Serverless Inbox with Sentiment Analysis, creating an Intelligent FAQ Chatbot using Retrieval-Augmented Generation, building a Subscription Prediction Model with SageMaker, and implementing an Emotion Recognition Service through Hugging Face. These projects are designed to provide practical experience in AI/ML engineering on AWS.
Who is the target audience for this course?
This course is aimed at individuals aspiring to become AI/ML Engineers with a focus on cloud-based solutions. It is suitable for those who wish to gain practical experience with AI APIs and AWS, and who are interested in developing skills in sentiment analysis, NLP, and computer vision.
How does the course depth compare to other AI/ML courses?
The course emphasizes practical, project-based learning, focusing on real industrial scenarios and modern AI APIs. It offers hands-on experience with AWS tools and services, such as SageMaker and serverless architectures, which may not be covered comprehensively in more theoretical courses.
What specific tools or platforms will I learn to use?
Students will learn to use various AWS services, including Amazon SageMaker for model building and deployment, and manage serverless architectures for efficient AI system integration. Additionally, the course covers the use of NLP techniques and computer vision tools, including those from Hugging Face.
What topics are not covered in this course?
The course does not cover foundational machine learning theory in depth, nor does it address platforms outside of AWS extensively. It focuses on practical application rather than the underlying theoretical aspects of AI/ML algorithms.
How can the skills learned in this course benefit my career?
Skills gained from this course, such as designing serverless architectures, mastering NLP and sentiment analysis, and deploying machine learning models on AWS, are highly applicable in various industries. These skills enhance your capability to solve real-world problems and are transferable to roles in AI/ML engineering, data analysis, and cloud solution development.