From machine learning model to real world application: Exploring AWS
Explore how AWS can support the journey from training a machine learning model to integrating its predictions into an application. Learn about model development, deployment options, API integration, and important cost and security considerations.
As an Artificial Intelligence and Machine Learning student, I find it interesting that training a model is only one part of developing an AI-powered application.
A model may perform well in a notebook, but how can other people use its predictions through a website or mobile application?
Cloud computing provides tools that can help connect machine learning models with real applications.
A model may perform well in a notebook, but how can other people use its predictions through a website or mobile application?
Cloud computing provides tools that can help connect machine learning models with real applications.
The first step is to prepare a dataset and train a model using an appropriate machine learning framework.
For example, a student might build a model that classifies images, predicts a numerical value, or categorises text.
Before deployment, the model should be evaluated using appropriate metrics and data that were not used for training.
AWS offers several services that support different parts of the machine learning workflow.
Amazon SageMaker AI: Provides capabilities for building, training, and deploying machine learning models.
Amazon S3: Can store datasets, model artefacts, and other project files.
AWS Lambda: Can execute application logic that connects other components, subject to its execution and resource limits.
The right combination depends on the model, workload, and deployment requirements.
Imagine creating a website that classifies images.
A simplified workflow could be:
User uploads an image → Frontend sends a request → Backend processes the request → Model generates a prediction → Website displays the result
Depending on the design, the backend might call a deployed model endpoint or invoke another inference mechanism.
The frontend should not contain secret credentials or directly expose privileged access to cloud resources.
For example, a student might build a model that classifies images, predicts a numerical value, or categorises text.
Before deployment, the model should be evaluated using appropriate metrics and data that were not used for training.
AWS offers several services that support different parts of the machine learning workflow.
Amazon SageMaker AI: Provides capabilities for building, training, and deploying machine learning models.
Amazon S3: Can store datasets, model artefacts, and other project files.
AWS Lambda: Can execute application logic that connects other components, subject to its execution and resource limits.
The right combination depends on the model, workload, and deployment requirements.
Imagine creating a website that classifies images.
A simplified workflow could be:
User uploads an image → Frontend sends a request → Backend processes the request → Model generates a prediction → Website displays the result
Depending on the design, the backend might call a deployed model endpoint or invoke another inference mechanism.
The frontend should not contain secret credentials or directly expose privileged access to cloud resources.
Deploying a model involves more than making predictions available.
We should consider:
Model accuracy and reliability.
Data privacy and access permissions.
Inference latency.
Resource requirements.
Monitoring and error handling.
Cost of training and inference.
A model that works on a small dataset may behave differently when exposed to real-world inputs.
We should consider:
Model accuracy and reliability.
Data privacy and access permissions.
Inference latency.
Resource requirements.
Monitoring and error handling.
Cost of training and inference.
A model that works on a small dataset may behave differently when exposed to real-world inputs.
A useful first project is a simple image-classification application.
Start by training a small model locally. Then explore how the model could be deployed through an appropriate AWS service and accessed through a backend API.
Document the architecture, test the model with new inputs, and review the costs associated with the selected deployment approach.
For a learning project, choose a small workload and review current AWS pricing before provisioning resources.
Start by training a small model locally. Then explore how the model could be deployed through an appropriate AWS service and accessed through a backend API.
Document the architecture, test the model with new inputs, and review the costs associated with the selected deployment approach.
For a learning project, choose a small workload and review current AWS pricing before provisioning resources.
Machine learning becomes more useful when its outputs can be integrated into applications that people can actually use.
AWS provides services that support model development, deployment, and application integration. Learning how these components connect can help students move from experimenting with algorithms to building complete AI-powered solutions.
My next step is to build a small AI application and document its architecture, deployment process, and limitations.
AWS provides services that support model development, deployment, and application integration. Learning how these components connect can help students move from experimenting with algorithms to building complete AI-powered solutions.
My next step is to build a small AI application and document its architecture, deployment process, and limitations.
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