ibraam-nashaat / vehiclevision

VehicleVision leverages AWS services to train and deploy an image classification model that can differentiate between bicycles and motorcycles.

VehicleVision

Project Overview

The core objective of VehicleVision is to craft an image classification model that excels at differentiating between bicycles and motorcycles.

Project Objectives

1. Precise Image Classification: Engineer a robust model capable of accurately categorizing images as bicycles or motorcycles.

2. Scalable Deployment: Utilize AWS Sagemaker to deploy the model in a scalable manner, accommodating varying demand.

3. Automated Workflow: Develop AWS Lambda functions to streamline data preprocessing and orchestrate their execution using AWS Step Functions.

4. Thorough Testing: Construct a comprehensive testing and evaluation framework to ensure both the model and the workflow's dependability.

5. Monitoring and Maintenance: Implement mechanisms to actively monitor model performance and identify potential anomalies.

AWS Services Used

1. AWS Sagemaker: Leveraged for model training, deployment and model monitoring, enabling scalable and efficient machine learning operations.

2. AWS Lambda: Utilized to create serverless functions for serializing images, classify images, and result filtering.

3. AWS Step Functions: Employed to seamlessly orchestrate the execution of Lambda functions, creating a coherent and automated workflow.

4. AWS S3: Utilized as a storage solution for data staging, model artifacts, and intermediate outputs during various project phases.

Project Phases

Phase 1: Data Preparation

Prepare the dataset for model training:

1. Extract data from a designated source.
2. Transform data into a suitable format for training.
3. Load processed data into a suitable storage system.

Phase 2: Model Training and Deployment

Train and deploy the image classification model:

1. Utilize AWS's image classification algorithm for model training.
2. Deploy the trained model to AWS Sagemaker endpoint.
3. Configure AWS Model Monitor to track deployment performance.

Phase 3: Lambda Functions and Workflow Orchestration

Develop AWS Lambda functions and orchestrate their execution:

1. Create three distinct AWS Lambda functions:
   1. Serialize image (serializeImage.py)
   2. Classify image (classifyImage.py)
   3. Result filtering (filterInferences.py).
2. Design a workflow using AWS Step Functions to coordinate these functions (stepFunction.json).

• Step Function Workflow:

Phase 4: Testing and Evaluation

Thoroughly assess the workflow's effectiveness:

1. Invoke the step function with test data.
2. Validate successful and unsuccessful workflow executions.
3. Utilize SageMaker Model Monitor insights to visualize model behavior.