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Learn to Deploy Your Own AI Computer Vision Project

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MASTER THE FUNDAMENTALS OF AI AND COMPUTER VISION 

  • Build a solid foundation in computer vision, differentiating between AI and Machine Learning.

  • Explore real-world applications with expert-led lessons, enhancing your understanding of the Computer Vision process.

  • Learn to build and optimize your own models, gaining practical insights and skills.

  • Acquire proficiency in Tapway labeling tools, project decision-making, and deployment options.

In this training program, you'll have access to interactive classroom learning, you'll gain insights through real case studies, experience hands-on practical sessions and complete mini-projects with Tapway Platform & Edge AI Devices. This training is available in Public and Private program format.


OVER THE DURATION OF THIS COURSE, YOU'LL:
💡  Learn about computer vision foundations
💡  Learn about Artificial Intelligence vs Machine Learning
💡  Learn about the Computer Vision process
💡  Dive deep into real-world examples 
💡  Understand how to build a model 
💡  Know how to improve model performance
💡  Learn the Tapway labeling tools 
💡  Understand how to choose between project types
💡  Become familiar with the Tapway deployment options
💡  Get insight into our best practices and tips 


CONCEPTS YOU'LL LEARN FROM THIS COURSE
📖  Classes
📖  Classification
📖  Object Detection
📖  Segmentation
📖  Confidence score
📖  Continuous learning
📖  Inference
📖  Label instructions
📖  Model training
📖  Model performance analysis
📖  Precision and Recall
📖  Model performance improvement
📖  Overfitting
📖  Test, train and dev set
📖  True positive & negative
📖  False positive & negative


WHAT YOU'LL BE ABLE TO DO BY THE END OF THE COURSE
✅  Make an informed decision about choosing a project type
✅  Effectively use each labeling tool
✅  Understand how to consider and clarify all aspects of your computer vision use case
✅  Create and maintain a labeling guide
✅  Build your own computer vision model
✅  Improve the performance of your trained model
✅  Make an informed decision about choosing a deployment option
✅  Deploy your trained model to the edge and configure the edge software to run with real time video streams


WHO SHOULD COME
🎓 Production and/or Operation Managers
🎓 Technical Managers
🎓 IT Managers
🎓 Quality Control and Quality Assurance
🎓 Technicians
🎓 Students and professionals in computer science, engineering, and related fields
🎓 Anyone who is interested in learning how computers can see

WHAT TO BRING
- Laptop (to be provided by the participant)

Course Agenda

Day 1 Indicative Hours of Learning: 8 hours
9.00 AM – 1:00 PM
  • Introduction to Deep Learning / Artificial Intelligence
    • What’s the difference between AI vs ML? 
  • Introduction to Computer Vision
    • Popular AI models
    • Evolution of CNN and Vision Transformers
    • Computer vision model types i.e. Object detection, Classification, Segementation
    • AI Computer vision process
    • Examples
1:00 PM  – 2.00 PM Lunch break
2:00 PM - 5:00 PM
  • Deep dive into AI Computer Vision workflow
    • How many images do you need to train a model?
    • Considerations for choosing the best images for your AI model
    • Do’s and don'ts of image curation
    • Considerations for choosing the appropriate “classes”
    • Labeling guide book
    • Labeling and quality checking process
    • Fast vs advanced training
    • Data augmentation and transformation
    • Analyze model performance
    • Improve model performance
    • Dataset
    • Label datasets
    • Model Training
  • Practical Hands-on Training Exercise for Datasets and Training Modules
Day 2 Indicative Hours of Learning: 8 hours
9.00 AM – 1:00 PM
  • Deployment
    • Make Predictions on Your Model
    • Cloud Predict Endpoint Deployment
    • Edge Software Deployment
  • Edge AI Software Setup Process
    • Camera or source setup
    • Inference pipeline builder
    • Middleware pipeline builder
1:00 PM  – 2.00 PM Lunch break
2:00 PM - 5:00 PM
  • Practical Hands-on Training Exercise for Edge Software Deployment
    • Deployment of Edge AI software to a test device
    • Deployment of Edge AI software to a cloud Virtual Machine (VM) instance
    • AI Inference Pipeline Builder and Configuration
  • Wrap up


Learning Objectives

1. Introduction to Deep Learning/Artificial Intelligence
2. Introduction to Computer Vision
3. Deep dive into AI Computer Vision workflow
4. Hands-on exercise
5. Model Training
6. Deployment
7. Edge Software Setup Process
8. Practical lab session

Content Delivery Method

Physical

HRD Corp Certified Course

Yes

Duration and Language

2 Days, English

Target Audience

Suitable for all employees with basic software development skills

Key Skillset Addressed

1. Make an informed decision about choosing a project type
2. Effectively use each labeling tool
3. Understand how to consider and clarify all aspects of your computer vision use case
4. Create and maintain a labeling guide
5. Build your own computer vision model
6. Improve the performance of your trained model
7. Make an informed decision about choosing a deployment option
8. Improve the performance of your trained model
9. Deploy your trained model to the edge and configure the edge software to run with real time video streams