Level 5: Choosing and feeding databases by sensor data logging for AI Machine Learning
Select the right database for your / industrial AI usecase
Identify a suitable database, consider industrial use cases, describe the USP of your targeted algos and consider how it could solve problems or benefit solutions.
Level 5 focuses on recording and analyzing sensor data. Students commission a sensor device, visualize and analyze its data in a database in order to later develop algorithms for machine learning and predictive models. The focus is on the selection of a suitable database for target achievement and automation in real time. At the end of this level, students will implement a data logger to use the data for smart AI-assisted solutions.
Summary for this Level
The fifth level introduces students to the exciting field of machine learning. They will use a sensor and store it's measured data in a cloud-based database for machine learning purposes, prepareing for platforms like Edge Impulse to use the data for training an algorithm. Additionally, students will select from various features of databases to match these with the unique selling propositions (USPs), they might have in mind for their project. Possibly they consider the potential for establishing an own startup around their project, which could become a product. This level combines technical skills in infrastructure with entrepreneurial thinking, preparing students to not only develop innovative solutions but also to consider marketing database features like viewing option on historic data and potentially commercialize their electronics project.
| Level of Difficulty: advanced / AI related | Tutor: Cynthia |
Tutorial for this level
Motivational or Reference Video
Gained Tech Competences
Remembering: In Level 5, students explore machine learning by first selecting an appropriate database of their choice. Understanding: They will explore different databases for data storage and analysis, e.g. ThingSpeak, InfluxData and Firebase. Apply: Setting up their device to feed data into a database like
https://www.thingspeak.com
https://www.timescale.com
https://www.influxdata.com
https://io.adafruit.com
https://www.prometheus.io
https://firebase.google.com
At this stage, students may also be inclined to think about the possible commercialization of their device, for example by considering its possible unique selling proposition (USP) and potential start-up opportunities. Analyze: Focus on developing ML skills, data management and entrepreneurial thinking. Evaluating: The reward includes a ESP32-CAM with an optional dual adapter for a 0.66 OLED and one year of free access to the SQ-Cloud for data analytics and AI.
Gained Soft Skills
Remembering: In Level 5, students will develop critical thinking and problem-solving skills by training and implementing machine learning models. Understanding: They will enhance their communication skills by marketing their algorithms and describing their unique selling points. Applying: Entrepreneurial thinking is fostered as they consider startup opportunities. Analyzing: Time management is key for balancing ML training with data management tasks. Evaluating: Adaptability is required to work with various databases, and resilience is built as they refine their models.
Summary for this Level
The fifth level introduces students to the exciting field of machine learning. They will use a sensor and store it's measured data in a cloud-based database for machine learning purposes, prepareing for platforms like Edge Impulse to use the data for training an algorithm. Additionally, students will select from various features of databases to match these with the unique selling propositions (USPs), they might have in mind for their project. Possibly they consider the potential for establishing an own startup around their project, which could become a product. This level combines technical skills in infrastructure with entrepreneurial thinking, preparing students to not only develop innovative solutions but also to consider marketing database features like viewing option on historic data and potentially commercialize their electronics project.
