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Meet Juv Chan
I have been working as a Software Engineer for most of my 15-year career in technology. I progressed from embedded software development, to full-stack web and database development, to cloud-native application development and DevOps. Currently, my work focuses on cloud AI development and data engineering.
In 2015, I was searching for cost-effective ways to advance my career. I firmly believed that machine learning and AI skills were the most coveted and critical digital skills – in addition to cloud and software development. Since I already had extensive software development experience, I focused my development plan on expanding my skillset in data science, machine learning (ML) and AI.
When AWS DeepRacer launched at AWS re:Invent 2018, my interest in AWS ML grew significantly because it was unprecedented for developers to get hands-on with reinforcement learning and autonomous racing with an edge AI race car device. In fact, I took part in the AWS DeepRacer League at the AWS Summit Singapore 2019 and was fortunate to win!
Scholarships Paving Way for Increased Opportunities
I applied for the Udacity AWS DeepRacer Scholarship Challenge and was awarded the full scholarship to the Udacity Machine Learning Engineer Nanodegree program in late 2019. I graduated from the program in 2020. As a graduate from the program, I learned new skills and reinforced existing skills in software engineering best practices, object-oriented programming, cloud services, machine learning workflows (Exploratory Data Analysis, Data Cleaning, Feature Engineering, Model Development & Validation, Model Deployment to Production).
I also had the opportunity to watch machine learning case studies from the well-taught course videos, and take part in hands-on assignments and a capstone project. This program also helped me prepare for and pass the AWS Certification exam for the machine learning specialization on the final day at AWS re:Invent 2019, even before I graduated from the program.
What I’ve Been Able to Build
Using what I learned from the Udacity ML Engineer Nanodegree, I built a sentiment analysis recurrent neural network (RNN) model hosted on a AWS static website with AWS Lambda and Amazon API Gateway integration that predicts the sentiment of a movie review, a plagiarism detector that examine classifies text files as either plagiarized or not, and a dog breed classifier which uses the image of a dog to classify it from a database of 133 breeds.
For the dog breed classifier, I have built the model with different deep convolutional neural network (CNN) model architectures (i.e. vanilla CNN versus transfer learning from pretrained VGG-16 ImageNet model) and hyperparameters tuning to learn about their effects on the model training and evaluation results.
For development and testing before deployment to AWS, I used Amazon SageMaker Python SDK, AWS SDK for Python (Boto3) to test my model, algorithm and workflow for the machine learning projects on local machine for faster iterations and cost saving. Overall, the machine learning workflows conceptual and hands-on skills that I gained from this program are beneficial to my AI development career where I applied some of the skills to improve the Natural Language Understanding (NLU) model for my company’s AI chatbot.
What’s New with Udacity’s AWS Machine Learning Scholarship?
I have completed the Udacity AWS Machine Learning Foundation to brush up on my ML foundational skills and in the hopes of qualifying for one of the 325 scholarships to the Udacity Machine Learning Engineer Nanodegree. In this year’s foundational course, I learned about generative AI, programs that enable machines to use image, text and audio files to generate original content! The course features a module that allowed me to get hands-on experience with AWS DeepComposer, the AI keyboard that uses generative adversarial networks (GANs), to generate music.
I will continue my ML journey by learning and sharing some of my personal ML projects featuring Amazon SageMaker, AWS DeepRacer or AWS DeepComposer on my personal blog, social media channels and GitHub with the public. Thanks very much and love to hear your feedback.
Check out Juv’s Webinar
Learn about the opportunities Machine Learning presents for your career from AWS ML experts and Juv Chan, Machine Learning Engineer graduate. Watch it now!