Tensorflow project
Budget: $750 – $1,500 USD
We are an established company engaged in a project focused on the detection and analysis of gases and organic compounds using sensor technology and artificial intelligence. We are seeking a TensorFlow expert with a solid background in machine learning and sensor data processing. The successful candidate will be responsible for analyzing data from sensors provided through ThingsBoard and developing TensorFlow models for accurate detection and analysis of gas compounds. This role requires proficiency in model optimization and deployment, specifically for real-time applications using TensorFlow Lite on an ESP 32.
- Processing of Gas and Organic Compound Sensor Data:
- Work with data provided from ThingsBoard, related to two main sensors focused on the detection of gases and organic compounds.
- Analyze and process this data to prepare it for modeling in TensorFlow.
- Design of Machine Learning Models and Neural Networks in TensorFlow:
- Develop TensorFlow-based machine learning models specializing in identifying and analyzing patterns of gases and organic compounds.
- Create AI solutions to enhance the accuracy and performance of the sensors in detecting these compounds.
- Implementation of Machine Learning Algorithms and Transfer Learning:
- Apply advanced machine learning and transfer learning techniques to improve sensor calibration and detection.
- Utilize reference data to enrich and refine the detection and analysis of gases and organic compounds.
- Optimization and Fine-Tuning of AI Models for Real-Time Detection:
- Optimize AI models to ensure their effectiveness in real-time detection of gases and compounds.
- Prepare the models for deployment in TensorFlow Lite, ensuring compatibility with an ESP 32.
- Documentation and Reproducibility of the Project:
- Thoroughly document the development and implementation of AI models, including data analysis, coding, and test results.
- Maintain a high level of transparency and reproducibility in each phase of the project.
All work has to be done in our servers and milestones will be discussed.
- Processing of Gas and Organic Compound Sensor Data:
- Work with data provided from ThingsBoard, related to two main sensors focused on the detection of gases and organic compounds.
- Analyze and process this data to prepare it for modeling in TensorFlow.
- Design of Machine Learning Models and Neural Networks in TensorFlow:
- Develop TensorFlow-based machine learning models specializing in identifying and analyzing patterns of gases and organic compounds.
- Create AI solutions to enhance the accuracy and performance of the sensors in detecting these compounds.
- Implementation of Machine Learning Algorithms and Transfer Learning:
- Apply advanced machine learning and transfer learning techniques to improve sensor calibration and detection.
- Utilize reference data to enrich and refine the detection and analysis of gases and organic compounds.
- Optimization and Fine-Tuning of AI Models for Real-Time Detection:
- Optimize AI models to ensure their effectiveness in real-time detection of gases and compounds.
- Prepare the models for deployment in TensorFlow Lite, ensuring compatibility with an ESP 32.
- Documentation and Reproducibility of the Project:
- Thoroughly document the development and implementation of AI models, including data analysis, coding, and test results.
- Maintain a high level of transparency and reproducibility in each phase of the project.
All work has to be done in our servers and milestones will be discussed.