Deploying TensorFlow Models on the Cloud

Day 9 of our TensorFlow Deep Learning Framework

Welcome to Day 9 of our 10-Day DIY TensorFlow Deep Learning Framework Setup series! Today, we’re taking our model deployment to the next level by exploring cloud deployment. Deploying on the cloud allows you to serve your model at scale, making predictions accessible to a broader audience.

Choosing a Cloud Platform

Several cloud platforms support TensorFlow model deployment, including Google Cloud AI Platform, Amazon SageMaker, and Microsoft Azure ML. Each platform has its own set of tools and services, but the deployment process generally involves the following steps:

• Model Export: Export your trained model in the SavedModel format, similar to what we did for local deployment.
• Cloud Storage: Upload the SavedModel to a cloud storage service like Google Cloud Storage, Amazon S3, or Azure Blob Storage.
• Model Deployment: Deploy the model using the platform’s deployment service, specifying the location of the SavedModel.
• Endpoint Configuration: Configure an endpoint for your model, making it accessible via an API.
• Scalability and Monitoring: Take advantage of cloud services for scalability and monitoring, ensuring your model performs well under varying workloads.

Google Cloud AI Platform Example

Let’s walk through a simple example using Google Cloud AI Platform for deployment. Before proceeding, make sure you have a Google Cloud Platform (GCP) account and have set up a project.

Export Model: Export your trained model as a SavedModel.

Upload to Cloud Storage: Upload the SavedModel to a Google Cloud Storage bucket:

   gsutil cp -r my_model gs://your-bucket-name/

Deploy Model: Deploy the model on Google Cloud AI Platform:

   gcloud ai-platform models create your_model

Create Version: Create a version for your model:

   gcloud ai-platform versions create v1 --model your_model --origin gs://your-bucket-name/my_model

Accessing the API: 3Once deployed, you can make predictions using the model’s API endpoint.

Amazon SageMaker Example

For Amazon SageMaker, the process is similar:

1. Export Model: Export your model as a SavedModel.
2. Upload to S3: Upload the SavedModel to an S3 bucket.
3. Create Model: Create a SageMaker model using the S3 location.
4. Deploy Endpoint: Deploy the model as an endpoint on SageMaker.
5. Accessing the API: Access predictions through the SageMaker endpoint.

Microsoft Azure ML Example

For Microsoft Azure ML:

1. Export Model: Export the model as a SavedModel.
2. Upload to Blob Storage: Upload the SavedModel to an Azure Blob Storage container.
3. Register Model: Register the model on Azure ML using the Azure portal or SDK.
4. Deploy Model: Deploy the registered model as a web service endpoint.
5. Accessing the API: Access predictions through the Azure ML web service endpoint.

What’s Next?

You’ve successfully deployed your TensorFlow model on the cloud, making predictions accessible at scale! In our final day, we’ll wrap up the series with best practices, additional resources, and next steps for your deep learning journey.

Stay tuned for Day 10: Conclusion and Next Steps. Happy coding!