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 skill set in data science, machine learning (ML) and artificial intelligence (AI).
When AWS DeepRacer launched at AWS re:Invent 2018, my interest in AWS ML grew. 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 and validation, model deployment to production).
I also had the opportunity to watch machine learning case studies from the well-taught course videos and participate 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’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 425 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 projects featuring Amazon SageMaker, AWS DeepRacer or AWS DeepComposer on my personal blog, social media channels and GitHub with the public.
What Kept Me Going During the Nanodegree Program?
The Udacity community offers a unique experience. One can easily interact with people from diverse backgrounds and discuss the questions or concepts in the Nanodegree program through specialized study groups. It is really encouraging to have a group of like-minded people who are happy to help and support you. No other online education provider offers such a robust and interactive global community like Udacity.
Another outstanding feature was the support I received from the mentors. Most of the feedback I received from the mentors on my projects was positive and constructive.
They told me exactly what could make my projects better and shared learning points that met all the requirements. The constructive and actionable feedback gave me the confidence I needed to go above and beyond in my job.
What I’ve Been Able to Build
Using what I learned from the Udacity Machine Learning Engineer Nanodegree, I built a sentiment analysis recurrent neural network (RNN) model hosted on a AWS static website.
The model was built with an 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 that 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 that 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 my local machine for faster iterations and cost saving.
Overall, I gained machine learning workflows conceptual and hands-on skills that 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.
Check out Juv’s Webinar
If you want to learn about the opportunities machine learning presents for your career, then watch Juv Chan—a Udacity Machine Learning Engineer graduate and AWS expert—talk about how machine learning helped his career.
If you are interested in upskilling yourself in AWS Machine Learning then we have good news for you as the Udacity & AWS Machine Learning Scholarship is back.
Complete the form on the Scholarship page and follow the steps to start the AWS Machine Learning Foundations course now.