Let’s see how Machine Learning can be built in the cloud. We’ll explore AWS services that will help you boost your productivity on the path from research to production.
Path to production can be long and winding — let’s see how we can make it easier! We’ll explore AWS services that will help you boost your productivity on the path from research to production.
Outline:
- Introduction to Machine Learning in AWS
- Building, Training, Deployment
- Using TensorFlow models
- Model debugging and model monitoring
- Integrating other AI Services (Recognition, Personalize, Forecast etc.)
Speaker:
Łukasz Czarnecki — AI / Data Science Consultant at Amazon Web Services Watch on YouTube
Materials:
Jupyter Notebooks:
aws/amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker. – aws/amazon-sagemaker-examples
GitHub
aws
SageMaker Notebooks:
Example Notebooks – Amazon SageMaker
Your notebook instance contains example notebooks provided by Amazon SageMaker. The example notebooks contain code that shows how to apply machine learning solutions by using SageMaker. Notebook instances use the nbexamples Jupyter extension, which enables you to view a read-only version of an examp…
