Delivered online or on-site, these instructor-led live TinyML training courses provide interactive, hands-on experience in leveraging machine learning on ultra-low-power devices. This approach empowers the development of AI-driven applications within resource-constrained environments.
TinyML training is accessible through either "online live training" or "onsite live training" modalities. Online live training, often referred to as "remote live training," is conducted via an interactive remote desktop session. Conversely, onsite live training can be facilitated directly at your customer premises in Lille or at NobleProg corporate training centers located in Lille.
NobleProg -- Your Local Training Provider
Lille, Gare Flandres
NobleProg Lille, 21 Avenue le Corbusier, Lille, france, 59800
In front of Flandres TGV Train Station
From Lille train stations
From Lille-Flandres: the address is essentially right next to the station — about 1–3 minutes on foot.
From Lille-Europe: walk toward Lille-Flandres along Avenue Le Corbusier. It is roughly 5–10 minutes on foot.
So you don't need a bus or metro if you're arriving by train.
If you're coming by bus
The nearest bus stops to Avenue Le Corbusier include Lion d'Or and Jacquet. Several local lines serve the area, including 13, 86, L5 and L91.
This instructor-led, live training in Lille (online or onsite) is aimed at intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for AI-powered applications on energy-efficient hardware.
By the end of this training, participants will be able to:
Understand the fundamentals of TinyML and edge AI.
Deploy lightweight AI models on microcontrollers.
Optimize AI inference for low-power consumption.
Integrate TinyML with real-world IoT applications.
This instructor-led program in Lille assists beginners and intermediate learners in developing practical TinyML applications utilizing Raspberry Pi and Arduino. The curriculum encompasses data acquisition, model refinement, and edge implementation, ultimately guiding you toward creating efficient, real-world embedded AI prototypes.
This instructor-led training in Lille empowers advanced professionals to design, optimize, and deploy complete TinyML pipelines. Through practical labs, participants will learn to gather data, train low-power models, and validate real-world applications.
This instructor-led session in Lille empowers advanced professionals to secure TinyML workflows on edge hardware. You will master the implementation of privacy-centric methods, reinforcement of models against adversarial threats, and the application of best practices for secure data processing in resource-limited environments.
This instructor-led live training in Lille guides advanced professionals through the process of integrating TinyML into autonomous robotics. Learners will design optimized models, construct on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.
This 21-hour instructor-led course in Lille empowers intermediate professionals to deploy TinyML for smart agriculture. Learn to build lightweight models, integrate edge AI with IoT, and optimize for precision irrigation and pest detection in a hands-on lab setting.
This instructor-led live training in Lille focuses on implementing TinyML solutions for healthcare monitoring and diagnostics. Participants will learn to design models for real-time health data, optimize them for low-power wearables, and ensure clinical reliability. The course includes hands-on lab exercises.
This live, instructor-led training in Lille empowers advanced practitioners to refine TinyML models for resource-constrained embedded devices. Participants will apply quantization and pruning, build low-latency inference pipelines, and evaluate performance relative to memory and energy limits.
This instructor-led, live training in Lille (online or onsite) is designed for intermediate-level IoT developers, embedded engineers, and AI practitioners who want to apply TinyML for predictive maintenance, anomaly detection, and smart sensor solutions.
Upon completing this training, participants will be able to:
Grasp the core principles of TinyML and its role in IoT ecosystems.
Configure a TinyML development environment tailored for IoT projects.
Create and deploy machine learning models on low-power microcontrollers.
Apply TinyML techniques for predictive maintenance and anomaly detection.
Refine TinyML models to maximize power efficiency and minimize memory consumption.
This instructor-led, live training in Lille (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who want to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.
Upon completion of this training, participants will be able to:
Grasp the fundamentals of TinyML and its advantages for edge AI applications.
Configure a development environment tailored for TinyML projects.
Train, optimize, and deploy AI models on low-power microcontrollers.
Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
Optimize AI models to meet power efficiency and memory limitations.
This instructor-led, live training in Lille (online or onsite) is designed for engineers and data scientists at the beginner level who want to grasp the fundamentals of TinyML, investigate its practical uses, and implement AI models on microcontrollers.
Upon completing this training, participants will be capable of:
Grasping the core concepts of TinyML and why they matter.
Deploying lightweight AI models onto microcontrollers and edge devices.
Optimizing and refining machine learning models to minimize power usage.
Utilizing TinyML for real-world scenarios including gesture recognition, anomaly detection, and audio processing.
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