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Please contact Doulos to discuss your requirements for in-person individual and team training.
Essential Edge AI is designed for engineers who need a practical understanding of deploying trained Neural Network models to constrained edge devices.
From principles and procedures, to important rules and helpful tricks, the course enables attendees to appreciate the system’s perspective of embedding a deep learning model inference into an application and how to connect it to other parts of the system to make it useful.
The practical side of the training is based around hypothetical Edge AI applications that step through the process from planning and pre-processing, to creating Neural Network models, right through to inferencing and deployment. These exercises comprise approximately 50% of class time.
Deep Learning practitioners who have a trained model and wish to deploy the model for an application in one or more of the following constrained edge device types - Linux based single board computers (x64 or ARM), Neural Network Accelerators or 32-bit Microcontrollers (such as a Cortex-M4)
Please note that the course does not delve into details of how to train a deep learning model or the basics of different neural network architectures. These details are covered in the Practical Deep Learning course. This course does not discuss the inference of large models (such as natural language processing) which are likely to run on Cloud servers.
Attendees should be familiar with and have experience of working with neural network models or completion of Practical Deep Learning training. Specifically, you should have:
Attendees should also have good working knowledge of Python and C or C++ programming language.
Please contact Doulos directly to discuss and assess your specific experience against the pre-requisites.
Doulos training materials are renowned for being the most comprehensive and user-friendly available. Their style, content and coverage are unique in the training world and have made them sought after resources. The materials include:
Supervised learning • Inference engines at the edge • Edge AI components • Edge AI applications • Data acquisition
Practicals: Try out model training environments based on CPU and GPU
Data Planning & Pre-processing
CRISP-DM Methodology & MLOps for Edge AI • Use case of audio feature pre-processing • Split audio file for training • Conversion of audio segments to spectrogram • Training data preparation
Practicals: Use SoX (Sound eXchange) software to trim, filter, and plot Spectrograms • View Spectrograms before training • Organize spectrograms into folders for training • Work with Environment Sound Classification (ESC) dataset
Creating Neural Network (NN) Models for Edge
Review of different kinds of neural networks • Convolutional Neural Network (CNN) using 2D and 1D convolution • Simple Recurrent Neural Network model • Transfer Learning • Chaining NN models
Practicals: Code (using Keras) simple Convolutional and Recurrent neural network using small datasets • Write code to use pretrained models (such as MobileNet) for transfer learning
Terminology – univariate, multivariate, regression, classification • Pre-processing using Pandas • Time Series as supervised learning problem • Keras Time Series Generator API • Time Series classification using CNN • RNN for Time Series Data • Time Series Database
Practicals: Set up time series dataset as supervised learning task • Perform CNN based fingerprint analysis of sections of time series data
Edge AI Hardware
Constrained Inference platforms • Inference server • Linux based SBC • 32 bit Microcontroller • NN Accelerator • GPU • FPGA
Practicals: Take picture, play and record audio using SBC • Read sensor data using serial port from SBC and Microcontroller
Model Formats • Open Neural Network Exchange Format (ONNX) • TensorFlow Lite (TFLite) • Viewing Model Graph • Model Format Conversion • Model Quantization
Practicals: Train and convert Scikit-Learn (ML) model to ONNX • Train and convert Keras Models to ONNX and TFLite
Inferencing steps • Input Tensor Shape • ONNX runtime, TFLite Interpreter Python methods • TFLite C++ Classes
Practicals: Convert Scikit-learn model to ONNX format. Perform Inference of NN model using ONNX/TFLite formats in Python environment. Infer TFLite model using C++ classes
TinyML Implementation frameworks • TFLite Micro • Converting Keras Model • Setting up and using TFLite Micro Interpreter
Practicals: Quantize Fully Connected TFLite Model • Convert Model to C array for storing in MPU • Read sensor value and execute model using TinyML (TFLite Micro) on Cortex-M4 device
Neural Network Accelerator
Neural Network Accelerator platforms • Model compiler and workload partitioning • Executing Model on Accelerator • Working with multiple accelerators
Practicals: Quantize and compile CNN Model for NN Accelerator • Run compiled model on accelerator and compare execution time with SBC
MobileNet architecture •Object Detection using MobileNet based SSD • Object Detector Output • Decoding and boxing detected output
Practicals: Use object detection code for determining if a car has been parked beyond a stipulated time
Monitor Model Inference and Performance
Accessing Model Inference using MQTT • Model inference using REST API • Monitoring Model Drift
Practicals: Communicate model inference output using MQTT and Flask webserver
Deploy Model to Edge Device
Enumerate Model Inference Dependencies • Create Dockerfile for Model Inference Container • Push Model Inference Container to Edge Device
Practicals: Create ONNX/TFLite inference Dockerfiles • Use Dockerfile to create Docker container and test it on Edge device
Edge AI use cases
Edge AI application planning template • Discussion on creating Edge AI solution for different use cases
For on-site, team-based training, please contact Doulos about tailoring this course to suit your particular target hardware and software environments.
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