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Parkinson Detector App Deployment (Part2 iBest Workshop)

BeginnerGuided Project

Do you want to deploy a serverless AI model like a software engineer using technologies such as Docker containers and Kubernetes? This guided project will show you how to deploy a Parkinson detection app in 10 mins. On the one hand, this project does not require knowledge of front-end and back-end development. On the other hand, the model deployment comes at no cost! You get free resources on IBM Cloud to experiment with deploying the AI model you like and share the app.

4.8 (61 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Healthcare

Enrollment Count

  • 313

Skills You Will Learn

  • Python, Artificial Intelligence, Machine Learning

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 min

Platform

  • SkillsNetwork

Last Update

  • May 10, 2025
About this Guided Project
In this project, we will show you step-by-step how to use IBM Code Engine to deploy your AI application on IBM Cloud. IBM Code Engine is a fully managed, serverless platform that provides an abstraction for the underlying infrastructure required to deploy your apps and lets you focus on the source code only (such as the Python code). The following picture shows an example of the Parkinson's detection model. On the left-hand side, the user provides the input of a voice-recorded number and returns an output on the right-hand side. 

Parkinson detection app

Learning Objectives

  • Know how to wrap a Machine Learning model inside Gradio’s interface.
  • Understand containerization.
  • Have hands-on experience with containerization.
  • Become familiar with IBM Code Engine.
  • Know how to use Code Engine to create and store container images on IBM Cloud.
  • Deploy the Machine Learning app from the container image.
  • Learn good practices and troubleshooting with IBM Code Engine.

Prerequisites (optional)

There is no prerequisite for this project. We will teach you all the Python code and the Code Engine CLI commands. We will make sure you understand everything as you go through the steps of the project. If you don’t have a finished AI model, don’t worry. We will also teach you some basics of deploying containerized applications.

What are you still waiting for? Let’s jump right in!

Instructors

Sina Nazeri

Data Scientist at IBM

I am grateful to have had the opportunity to work as a Research Associate, Ph.D., and IBM Data Scientist. Through my work, I have gained experience in unraveling complex data structures to extract insights and provide valuable guidance.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Roxanne Li

Data Scientist at IBM

I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!

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Sheng-Kai Chen

Data Scientist

Sheng-Kai Chen is a graduate student at the University of Toronto, concentrating on Information Systems & Design. Having several experiences analyzing data for retail stores and designing small software for small businesses. Sheng-Kai was inspired to shift toward answering new challenges with machine learning and new technics.

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Alice Rueda

Postdoctoral Fellow

Alice is a postdoctoral fellow and AI Lead at the Interventional Psychiatry Program, St. Michael’s Hospital and iBEST Trainee Lead. Alice completed her doctoral degree in electrical engineering from Toronto Metropolitan University (formerly Ryerson University), Toronto, ON in 2021. After working in the industry for more than a decade, I decided to pursuit my doctoral degree in 2016. I received a bachelor degree in electrical engineering and a master degree in electrical and computer engineering from the University of Manitoba, Winnipeg, MB in 1994 and 1999, respectively. I was awarded an (honoris causa) Doctor of Laws degree from Brock University, St. Catharines, ON in 2020. I specialize in signal processing and applications of machine learning. I am currently serving as the Secretary for the IEEE Signal Processing Toronto Chapter, an affiliated member of the IEEE Machine Learning for Signal Processing, and a reviewer for IEEE conferences. Alice had also served as the Director of Machine Learning at Aggregate Intellect in 2022.

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