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.
