5 AWS Projects to Become an AI/ML Engineer

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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.

About the Author: Lucy Wang aka. Tech With Lucy

Lucy Wang aka. Tech With Lucy thumbnail
Some of you may know me from the YouTube channel Tech With Lucy, where I help job seekers and career changers develop technical skills and land their dream jobs in the cloud technology sector. My journey into cloud technology began almost four years ago. Most of this time, I worked as a Solutions Architect at AWS, helping small and medium-sized businesses implement cloud solutions into their processes. This job involved in-depth analysis of clients' tasks, finding optimal solutions, and designing architectures using AWS services for business growth and scaling. During this time, I gained a solid understanding of cloud infrastructure, network architectures, and the principles of building scalable systems. Constant interaction with businesses taught me strategic thinking, the importance of clear communication, and a focus on real customer needs. In addition to my main job, on weekends I mentored and coached hundreds of students and job seekers on an individual basis. I assisted with resume building, preparation for technical interviews, and conducted mock interviews, which enabled many of them to take a confident step towards a new career.