Rzeszów

RISE, Plac Wolności 13, Rzeszów, Poland, 35-073

The training room is located in the very heart of Rzeszow, making it easily accessible for participants. In the immediate vicinity, there are major public transportation hubs, such as city buses (MPK), railways (PKP), and long-distance buses (PKS), facilitating access from various parts of the city and beyond. Additionally, there is an underground parking garage at the Center Park gallery nearby, providing convenient parking for those using their own vehicles.

Głogów Małopolski

NobleProg , Strefowa 1, Głogów Małopolski, Poland, 36-060

The training room is located just 10 km southwest of Rzeszow, directly on the Rzeszow-Radom route, providing easy access from both cities. Additionally, its proximity to the A4 motorway and Jasionka airport facilitates transportation for both car travelers and those utilizing air transport.

Explore Our Courses

TinyML: Running AI on Ultra-Low-Power Edge Devices

 21 Hours
This instructor-led, live training in podkarpackie (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.
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TinyML for IoT Applications

 21 Hours
This instructor-led, live training in podkarpackie (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications. By the end of this training, participants will be able to:
  • Understand the fundamentals of TinyML and its applications in IoT.
  • Set up a TinyML development environment for IoT projects.
  • Develop and deploy ML models on low-power microcontrollers.
  • Implement predictive maintenance and anomaly detection using TinyML.
  • Optimize TinyML models for efficient power and memory usage.
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Deploying AI on Microcontrollers with TinyML

 21 Hours
This instructor-led, live training in podkarpackie (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse. By the end of this training, participants will be able to:
  • Understand the fundamentals of TinyML and its benefits for edge AI applications.
  • Set up a development environment for TinyML projects.
  • Train, optimize, and deploy AI models on low-power microcontrollers.
  • Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
  • Optimize AI models for power efficiency and memory constraints.
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Introduction to TinyML

 14 Hours
This instructor-led, live training in podkarpackie (online or onsite) is aimed at beginner-level engineers and data scientists who wish to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers. By the end of this training, participants will be able to:
  • Understand the fundamentals of TinyML and its significance.
  • Deploy lightweight AI models on microcontrollers and edge devices.
  • Optimize and fine-tune machine learning models for low-power consumption.
  • Apply TinyML for real-world applications such as gesture recognition, anomaly detection, and audio processing.
Read more...

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