Image classification on Amazon SageMaker

In a previous video, I introduced you to Amazon SageMaker, a fully-managed service for Machine Learning. You learned about the different ways you could use SageMaker: using built-in algorithms, bringing your training script, bringing your own model and even bringing your own custom training and prediction code.

Enough cats on the Internet already!
Enough cats on the Internet already!

Today, I’m going to focus on using the built-in algorithm for image classification. In this code-level video, you will learn how to:

  • train an image classification model on your own image data set,
  • either train from scratch or fine-tune a pre-trained network,
  • access and plot training logs stored in Amazon CloudWatch,
  • use your trained model to classify real-life images.

Here we go!


About the Author

Julien Simon is the Chief Evangelist at Arcee AI , specializing in Small Language Models and enterprise AI solutions. Recognized as the #1 AI Evangelist globally by AI Magazine in 2021, he brings over 30 years of technology leadership experience to his role.

With 650+ speaking engagements worldwide and 350+ technical blog posts, Julien is a leading voice in practical AI implementation, cost-effective AI solutions, and the democratization of artificial intelligence. His expertise spans open-source AI, Small Language Models, enterprise AI strategy, and edge computing optimization.

Previously serving as Principal Evangelist at AWS and Chief Evangelist at Hugging Face, Julien has authored books on Amazon SageMaker and contributed to the open-source AI ecosystem. His mission is to make AI accessible, understandable, and controllable for everyone.