Machine Learning from the Cloud to the IoT
Machine Learning and the IoT are a match made in heaven. After all, IoT devices collect mountains of sensor data, what better way to uncover insights and actions than through sophisticated, modern computing methods like ML and AI?
The problem is, leveraging ML with IoT has historically meant backhauling all your sensor data to the Cloud. When the cloud is involved, security is a concern, and in the realm of IoT, security is often a dirty word.
In this workshop, you’ll learn how to leverage the power of Machine Learning on Edge Devices and build secure, cloud-independent IoT applications.
Section 1: The Basics of Machine Learning
Session 1: Demystifying AI, ML, etc.
- Lab 1: Building your first ML Model
- Session 2: ML models for Complex Data: CNNs and LSTMs
- Lab 2: Building a CNN for Image Recognition
Section 2: The Basics of IoT
- Session 1: Introducing the Blues Wireless Ecosystem
- Lab 1: Claiming your first Device
- Session 2: Working with Sensors and Actuators to collect data
- Lab 2: Working with Sensor Data
Section 3: ML and the IoT
- Session 1: Why ML and the IoT
- Session 2: ML at the Edge with Single Board Computers (SBCs)
- Lab 2: Building ML Models for Edge Inferencing
- Lab 3: Inferencing on SBCs
- Session 3: ML at the Edge with Microcontrollers (MCUs)
- Lab 4: Building ML Models for Microcontrollers
- Lab 5: Inferencing on MCUs
Section 4: The future of ML and the IoT
- Session 4: What's next for ML on the Edge?
- Lab 1: Hacking Challenge and Free Exploration
Brandon is the Director of Developer Experience at Blues Wireless, the founder of Carrot Pants Press, a maker education and publishing company, and an avid tinkerer and maker.