Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
34. Auto Scaling group
Availability Zone 1
Availability Zone 2
Availability Zone 3
Deployment / Hosting
Amazon SageMaker ML
Compute Instances
SageMaker Endpoints
Elastic
Load Balancing
Model
Endpoint
Amazon
API
Gateway
Input Data
(Request)
Prediction
(Response)
Client
Model
saved in S3
Elastic
Container
Registry
Trained
model
Inference
container
41. Appendix – other useful links
https://sagemaker.readthedocs.io/en/stable/framework
s/pytorch/using_pytorch.html#train-a-model-with-
pytorch
Using PyTorch
script
documentation
Github Sagemaker
Examples
https://github.com/aws/amazon-sagemaker-examples
Github using
PyTorch script
example
https://github.com/aws/amazon-sagemaker-
examples/tree/master/sagemaker-python-
sdk/pytorch_cnn_cifar10
Note: there has been a recent update of the SageMaker SDK to version 2.0. Some examples are written for the previous SageMaker SDK. Please,
notice this link for further details:
https://sagemaker.readthedocs.io/en/stable/v2.html
You may downgrade temporarily with the following terminal command:
pip install sagemaker==1.72.0 –U
Upgrading to the latest SageMaker SDK can be done by executing:
pip install --upgrade sagemaker