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The complete Course to Build on-Device AI Applications

Kumari Ravva

You will learn how to Build on-Device AI Applications in this course.  On-device AI applications are rapidly transforming how artificial intelligence is deployed, offering powerful advantages in terms of performance, privacy, and energy efficiency. Unlike cloud-based AI, which relies on sending data to external servers for processing, on-device AI performs computations locally on a user’s device, such as a smartphone, smartwatch, or IoT sensor. This shift in paradigm is reshaping industries by enabling faster decision-making, improving security, and reducing latency in real-time applications.

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You will learn how to Build on-Device AI Applications in this course.  On-device AI applications are rapidly transforming how artificial intelligence is deployed, offering powerful advantages in terms of performance, privacy, and energy efficiency. Unlike cloud-based AI, which relies on sending data to external servers for processing, on-device AI performs computations locally on a user’s device, such as a smartphone, smartwatch, or IoT sensor. This shift in paradigm is reshaping industries by enabling faster decision-making, improving security, and reducing latency in real-time applications.

one of the benefits of on-device AI is reduced latency. By eliminating the need to send data back and forth to a remote server, AI models can process information instantly. This is critical for applications requiring real-time responses, such as autonomous driving, augmented reality (AR), and virtual assistants. For instance, a self-driving car must detect and react to objects in its environment in milliseconds, something that cloud computing alone cannot guarantee due to potential delays in communication. On-device AI also enhances user privacy. By keeping sensitive data on the device, the risk of exposure during transmission to external servers is minimized.

on-device AI is unlocking new opportunities across various industries, enabling more responsive, private, and energy-efficient applications. As hardware and software innovations continue to evolve, the potential for on-device AI will only grow, offering even more sophisticated and ubiquitous intelligent experiences.

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What's inside

Learning objectives

  • Be able to build on-device ai applications
  • Learn how to build and deploy the application into various devices
  • Have the knowledge to build responsive and energy-efficient applications.
  • Build some applications with ai

Syllabus

Introduction
Build on-Device AI Applications Introduction
Build AI Applications Overview
Creating an E-Commerce Website with AI
Read more
AI components with API
Building AI applications
Building an E commerce Webportal with AI
Buiding Functionality
Cloning Repository
Create Complex Design pattern
Forking and pull Request
Github Private Repos
Line and Trunecates
Optional Challenge
Tableview Delegate
UserDefaults
Version Control Repositiry
Adding a delegate Method
Adding colors to App
Configure Core Data
Creating Gradient flows cells
Encoding Data with NSCoder
Fetching Data from Realm Data
Persistant Local Data Storage
Querying data using Realm
Restructring Our App
Updating data with core data
AI Tools for designing context
building ToDo Application with AI
Adding materials ICON to app
Combine Fronts Typography
Differences in ICON Guidelines
Gather Data for training
How to Use Intention Actions
Installation for Troubleshooting
Prototyping with keynotes
Supervised Leaning
Swift Structs
Tools for Designing with colors
Code Refactoring
Content API
Encapsulations in Actions
How to keep designing and upating
How to use Bootstap to build your apps
Import the image Recognition
Layout of the application
Natural Language Processing
Resetting the measurements
Update the state of application
How to AI for getting image recognition
Advanced Properties
Container Wedgets
Getting Image Recognization
How to create wireframe
Importance of making an App
Making Https Requests
Reinforcement Learning
Restoring In-app Repository
Tools for creating various mockups
Usability functions
Adding Tracking Images
How to add apps to device
How to create a landing page website
How to use CANVA for mockups
Making Batch Predictions
Observed Properties
Parsing Json Result
Sending outbound emails
Setup your inbuild App
Useful tools for app Submission
Deployment of AI Applications
Dealing with Decimal
Deploying Designed apps into production
How to add Plane to cards
How to Source the assests
Installing Core tools
Performing Classification
Set up Picker Controller
Swift Access Levels
Updating User Inferface
Pagination of update records
AI Powered componets

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in The complete Course to Build on-Device AI Applications with these activities:
Review Machine Learning Fundamentals
Review fundamental machine learning concepts to better understand the AI models used in on-device applications. This will help you grasp the underlying principles and limitations of these models.
Show steps
  • Review key concepts like supervised and unsupervised learning.
  • Study common machine learning algorithms.
  • Understand model evaluation metrics.
Review 'Practical Deep Learning for Cloud, Mobile, and Edge'
Gain a broader perspective on deep learning deployment across various platforms, including mobile and edge devices.
View Practical Deep Learning for Cloud, Mobile, and... on Amazon
Show steps
  • Read the book and take notes on key concepts.
  • Research the tools and frameworks discussed in the book.
Review 'TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers'
Gain practical knowledge of deploying machine learning models on resource-constrained devices, a core skill for on-device AI development.
View Basic Course Learning About TinyML on Amazon
Show steps
  • Read the book and try out the example projects.
  • Experiment with deploying your own models on microcontrollers.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow TensorFlow Lite Tutorials
Enhance your skills in on-device AI development by following TensorFlow Lite tutorials. These tutorials provide step-by-step guidance on model conversion, optimization, and deployment.
Show steps
  • Find TensorFlow Lite tutorials relevant to your interests.
  • Follow the tutorials, paying attention to the code examples.
  • Experiment with modifying the code to suit your own projects.
Build a Simple Image Recognition App
Solidify your understanding of on-device AI by building a practical image recognition application. This project will involve model deployment, data handling, and user interface design.
Show steps
  • Choose an image recognition model suitable for on-device deployment.
  • Integrate the model into a mobile application.
  • Design a user interface for image capture and result display.
  • Optimize the application for performance and energy efficiency.
Write a Blog Post on On-Device AI Benefits
Reinforce your understanding of the advantages of on-device AI by writing a blog post explaining its benefits, such as reduced latency and improved privacy.
Show steps
  • Research the key benefits of on-device AI.
  • Outline the structure of your blog post.
  • Write the blog post, providing clear explanations and examples.
  • Edit and proofread your blog post.
Contribute to a TinyML Open Source Project
Deepen your understanding of on-device AI by contributing to an open-source TinyML project. This will give you hands-on experience with real-world challenges and collaborative development.
Show steps
  • Find a TinyML open-source project that interests you.
  • Review the project's documentation and contribution guidelines.
  • Identify a bug or feature to work on.
  • Submit a pull request with your changes.

Career center

Learners who complete The complete Course to Build on-Device AI Applications will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer focuses on developing, deploying, and maintaining machine learning models, including those for on-device AI. Since this course focuses directly on building AI applications that run on devices, it is going to be very helpful. Machine Learning Engineers benefit from learning how to optimize AI models for on-device performance to ensure low latency and high efficiency. The course's coverage of topics like supervised learning, reinforcement learning, and deployment strategies give machine learning engineers the skills they need.
See salaries and explore the career path for Machine Learning Engineer
Mobile Application Developer
A Mobile Application Developer designs and builds applications for mobile devices. This course helps build a foundation for developing on-device AI applications, a growing trend in mobile app development. Mobile Application Developers need to create applications that are responsive, energy-efficient, and capable of handling complex AI computations locally. With its focus on building and deploying AI applications on various devices, this course helps mobile developers get the knowledge to integrate AI functionalities directly into mobile apps. This course covers topics such as image recognition, natural language processing, and data storage, equipping developers with the skills to create intelligent mobile experiences by building ToDo applications, working with mockups, and more.
See salaries and explore the career path for Mobile Application Developer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer is responsible for developing and deploying AI models and systems. This course directly aligns with the role of an AI Engineer by focusing on building AI applications that run on devices. AI Engineers benefit from learning how to optimize AI models for on-device performance to ensure low latency and high efficiency. The course's coverage of topics like supervised learning, reinforcement learning, and deployment strategies helps AI Engineers build and deploy AI applications for various devices. Furthermore, the course's syllabus that covers gathering data for training and building AI components with existing APIs will be useful.
See salaries and explore the career path for Artificial Intelligence Engineer
Internet of Things Developer
An Internet of Things Developer creates software for IoT devices, which often require on-device AI capabilities. The course may be useful to IoT Developers because it teaches how to build and deploy AI applications on various devices. IoT Developers can use this knowledge to enhance the intelligence and autonomy of IoT devices, enabling them to process data locally and make real-time decisions. The fact that this course covers building responsive and energy-efficient applications is particularly well-suited to the power constraints often found in IoT deployments. Learning about topics such as gathering data for training and AI tools will allow them to enhance IoT devices.
See salaries and explore the career path for Internet of Things Developer
Augmented Reality Developer
An Augmented Reality Developer creates interactive experiences that overlay digital content onto the real world, frequently leveraging on-device AI for real-time object recognition and tracking. Augmented Reality Developers can use the skills taught in this course in a variety of ways. For example, they can apply it to build and deploy AI applications for various devices, such as smartphones and tablets. The course's syllabus covers topics like image recognition, natural language processing, and building AI components with existing APIs, which are all useful in Augmented Reality development.
See salaries and explore the career path for Augmented Reality Developer
Embedded Systems Engineer
An Embedded Systems Engineer designs and develops hardware and software for embedded systems, which increasingly incorporate on-device AI. This course may be useful, as it equips Embedded Systems Engineers with the skills to integrate AI functionalities directly into embedded devices. By focusing on building and deploying AI applications on various devices, they are suited to the constraints and requirements of embedded systems. With its emphasis on efficiency and performance, the course is particularly relevant for embedded systems, where resources are limited. The course also explores topics like version control repository, persistent data storage, and updating user interface, which are useful for this role.
See salaries and explore the career path for Embedded Systems Engineer
Computer Vision Engineer
A Computer Vision Engineer specializes in developing algorithms that enable computers to 'see' and interpret images, often employing on-device AI for real-time processing. You may find this course useful, as it gives you skills relevant to image recognition and processing on devices. Computer Vision Engineers can use the knowledge gained in this course to build applications for autonomous vehicles, surveillance systems, and augmented reality. The course's syllabus covers topics like image recognition and building AI components with existing APIs, which equips Computer Vision Engineers to work with images on devices.
See salaries and explore the career path for Computer Vision Engineer
Robotics Engineer
A Robotics Engineer designs, builds, and programs robots, often integrating on-device AI for autonomous navigation and decision-making. You may find this course helpful, as Robotics Engineers can use the knowledge gained from it to build applications for robots. The course covers topics such as supervised learning, reinforcement learning, and deployment strategies, which are directly applicable to the core responsibilities of a Robotics Engineer. Furthermore, they may be excited about building and deploying AI applications on various devices, thus empowering robots to be AI-enhanced.
See salaries and explore the career path for Robotics Engineer
Data Scientist
A Data Scientist analyzes data to extract insights and build predictive models, potentially utilizing on-device AI for real-time analysis. This course may be useful, as on-device AI enables faster decision-making, improved security, and reduced latency in real-time applications. Data Scientists can use this skill to build and deploy AI applications for various devices. The course covers topics such as supervised learning, reinforcement learning, and data storage, which are directly applicable to the core responsibilities of a Data Scientist. The course's syllabus also covers topics like gathering data for training, version control repository, and data storage, which are useful.
See salaries and explore the career path for Data Scientist
Software Architect
A Software Architect is responsible for designing the overall structure and components of software systems. Software Architects can benefit from learning how to design systems that incorporate on-device AI to enhance performance, privacy, and efficiency. This course may be useful for Software Architects, as it will broaden their understanding of the architectural considerations for on-device AI applications. The course also explores topics such as version control repository and persistent data storage, which are directly applicable to the responsibilities of a Software Architect.
See salaries and explore the career path for Software Architect
Data Engineer
A Data Engineer builds and maintains the infrastructure for data storage and processing. Data Engineers can use what they learn to build and deploy AI applications for various devices. This course may be useful, as the course covers topics such as persistent local data storage and dealing with decimal, which is directly applicable to the responsibilities of a Data Engineer. This course helps build a foundation for storing AI-generated data on local devices.
See salaries and explore the career path for Data Engineer
Computer Programmer
A Computer Programmer writes code to create software and applications. This course will help you grasp how to build and deploy AI applications for various devices using code. You may find this course useful, as this course covers topics such as Github private repositories, Swift structs, and Swift access levels, which are directly applicable to the responsibilities of a Computer Programmer. This course helps build a foundation for storing AI-generated data on local devices.
See salaries and explore the career path for Computer Programmer
Database Administrator
A Database Administrator is responsible for maintaining the integrity and security of databases. A Database Administrator can benefit from learning about building on-device AI, as they can learn how to train it and use it to improve their database and information retrieval capabilities. This course may be useful, as it provides Database Administrators with the knowledge to understand and articulate the potential of using AI within a database. The course covers topics such as configuring core data, fetching data, and querying data.
See salaries and explore the career path for Database Administrator
Tech Consultant
A Tech Consultant advises clients on how to use technology to meet their business needs. As technology shifts, the Consultant's insights must keep pace. The course provides Consultants with foundational knowledge of building on-device AI applications, enabling them to advise clients on the benefits, challenges, and potential applications of this technology. Learning about building E commerce webportals with AI, tools for designing context, and building applications for various devices will allow for more thorough and useful advising techniques and practices. Tech consultants may appreciate the broad overview this course offers.
See salaries and explore the career path for Tech Consultant
Product Manager
A Product Manager guides the vision, strategy, and roadmap for a product. This course may be useful, as it provides Product Managers with the knowledge to understand and articulate the potential of on-device AI in their product strategies. By understanding the performance, privacy, and energy efficiency advantages, Product Managers can make informed decisions about integrating on-device AI features. The course covers topics such as data storage and data processing, which gives Product Managers insight into how these strategies can contribute to the quality and adoption of products.
See salaries and explore the career path for Product Manager

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in The complete Course to Build on-Device AI Applications.
Cover image
Basic Course Learning About TinyML
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Provides a practical guide to deploying machine learning models on microcontrollers, which is directly relevant to on-device AI. It covers TensorFlow Lite Micro, a key framework for on-device AI development. Reading this book will give you hands-on experience with deploying models on resource-constrained devices. It serves as a valuable reference for understanding the practical challenges and solutions in on-device AI.
Basic Course Learning About TinyML
Paperback
Check
Basic Course Learning About TinyML
Kindle Edition
Check
Cover image
Practical Deep Learning for Cloud, Mobile, and Edge
Save
Offers a comprehensive overview of deep learning deployment across various platforms, including mobile and edge devices. It covers model optimization techniques, hardware considerations, and real-world applications. Reading this book will provide you with a broader perspective on the challenges and opportunities in deploying deep learning models in different environments. It useful reference for understanding the entire deployment pipeline.
Practical Deep Learning for Cloud, Mobile, and Edge
Paperback
Check
Practical Deep Learning for Cloud, Mobile, and Edge
Kindle Edition
Check

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Effort
31 total hours
Via
Udemy
Instructor
Kumari Ravva
Language
English
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