Description
Data Collection: Identify and Collect data from various web sources via web scraping for model training purpose.
Data Organization: Filter the obtained, clean it and prepare it for model training.
Model Prototyping: Theorize, search and code the model architectures relevant to specific problem at hand.
Model Training: Define network architecture and write the corresponding utility functions for training the model.
Metric Definition: Create a metric to evaluate the model based on the provided problem statement.
Model Testing: Test the trained mode using this defined metric.
Preferred Skills:
Model Deployment: Compile the model into functional application code for deployment.
Containerization: Create containers for the developed model to create an end-to-end pipeline.
Cloud Computing: Knowledge of cloud services such as AWS EC2, AWS S3, AWS SageMaker.
Required Knowledge:
CNN Based Architectures: LeNet, AlexNet, ZF Net, VGGNet, ResNet, MobileNets, InceptionNet
Segmentation Based Models: Fully Convolutional Network (FCN), U-Net, Faster RCNN, Mask RCNN.
Perks and Benefits -
5 Days working.
Certificate of Excellence.
Reference letter.