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You will get a fully optimized TensorFlow Lite model deployed on ESP32 or STM32
Project details
Are you looking to make your hardware intelligent without relying on slow, expensive, and internet-dependent cloud APIs?
I specialize in Edge AI and TinyML deployment, bringing deep learning directly to your resource-constrained microcontrollers (ESP32, STM32) and embedded systems. With professional background in industrial automation and precision robotics, I don't just write Python scripts; I build robust, production-ready C++ firmware.
Whether you need real-time acoustic signal classification, machine vibration analysis, or localized computer vision, I can optimize and quantize your heavy neural networks (like CNNs) into highly efficient TensorFlow Lite models. I ensure your hardware operates intelligently, with ultra-low latency, right at the edge.
My Tech Stack:
AI/ML: TensorFlow Lite, Signal/Acoustic Processing, Python
Embedded: C/C++, ESP32, STM32, Sensor Integration
Control Logic: PID, Automated Sorting, Robotics
Let's bypass the cloud and build truly autonomous, smart hardware.
I specialize in Edge AI and TinyML deployment, bringing deep learning directly to your resource-constrained microcontrollers (ESP32, STM32) and embedded systems. With professional background in industrial automation and precision robotics, I don't just write Python scripts; I build robust, production-ready C++ firmware.
Whether you need real-time acoustic signal classification, machine vibration analysis, or localized computer vision, I can optimize and quantize your heavy neural networks (like CNNs) into highly efficient TensorFlow Lite models. I ensure your hardware operates intelligently, with ultra-low latency, right at the edge.
My Tech Stack:
AI/ML: TensorFlow Lite, Signal/Acoustic Processing, Python
Embedded: C/C++, ESP32, STM32, Sensor Integration
Control Logic: PID, Automated Sorting, Robotics
Let's bypass the cloud and build truly autonomous, smart hardware.
Machine Learning Tools
Google Sheets, Keras, MATLAB, NumPy, OpenCV, Python, TensorFlowWhat's included
| Service Tiers |
Starter
$45
|
Standard
$145
|
Advanced
$295
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 2 | 3 | 4 |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 5 | 10 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - |
Optional add-ons
You can add these on the next page.
Model Documentation
+$40
Source Code
+$50
Additional Revision
(+ 1 Day)
+$20Frequently asked questions
About Feb
Hardware Design Engineer | PCB Layout & Schematics | KiCad
Karawang, Indonesia - 10:37 am local time
Drawing from my experience as a former Automation Engineer at Sharp Electronics, I understand the rigorous standards required for reliable mass manufacturing. Whether you need a custom microcontroller schematic (ESP32/STM32), complex multi-layer PCB routing, or DFM-compliant Gerber generation, I deliver robust boards designed for real-world environments.
My Hardware Tech Stack:
1. PCB Design & EDA: KiCad, Schematic Capture, 3D PCB Rendering.
2. Embedded Systems: ESP32, STM32, Custom Microcontroller Board Design.
3. Design for Manufacturing (DFM): Differential pairs routing, thermal management (vias), rule area constraints, and component footprint creation.
4. System Integration: Motor drivers, sensor interfaces, and power delivery networks (PDN).
I don't just connect pins; I ensure your hardware operates safely, efficiently, and is ready for automated factory assembly (PCBA).
Steps for completing your project
After purchasing the project, send requirements so Feb can start the project.
Delivery time starts when Feb receives requirements from you.
Feb works on your project following the steps below.
Revisions may occur after the delivery date.
Data Analysis & Feasibility Simulation
Reviewing the client's dataset, evaluating hardware operational constraints, and running an initial Python simulation to verify model capability.
Model Training & TFLite Optimization
Designing the neural network architecture, training the model using Python, and optimizing/quantizing it into a lightweight C++ array or .tflite format for edge deployment.