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Amazon SageMaker

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Machine Learning is more popular than ever as today we can leverage the ability of the machines to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. Howerer, Traditional machine learning development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. Amazon SageMaker Studio solves this challenge by providing all of the components used for machine learning in a single, web-based visual interface. That is why we have invited Julien Simon at the AWS Sofia to give us some insights on this extremely trending topic.

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Julien Simon and his introduction to SageMaker

Julien Simon is a Principal Evangelist at Amazon Web Services and holds all seven AWS certifications. He focuses on helping developers and organizations bring their ideas to life. He is particularly passionate about Machine Learning and how it could be done with Amazon Web Services. SageMaker is the first fully integrated development environment (IDE) for machine learning.

Traditional machine learning development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. Amazon SageMaker Studio solves this challenge by providing all of the components used for machine learning in a single, web-based visual interface. SageMaker Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. All ML development activities including notebooks, experiment management, automatic model creation, debugging, and model drift detection can be performed within the unified SageMaker Studio visual interface.

Deep Dive in Amazon SageMaker

During the Deep Dive Session on Amazon SageMaker at AWS Sofia Julien will explore advanced features of Amazon SageMaker that will help you boost your productivity, as well as the quality of your models. First, we’ll see how to detect and fix training issues with SageMaker Debugger.

The ML training process is largely opaque and the time it takes to train a model can be long and difficult to optimize. As a result, it is often difficult to interpret and explain models. Amazon SageMaker Debugger makes the training process more transparent by automatically capturing real-time metrics during training such as training and validation, confusion matrices, and learning gradients to help improve model accuracy. Then, Julien will also show you how to monitor models in production, and identify prediction issues such as missing features or data drift. Amazon SageMaker Model Monitor allows developers to detect and remediate concept drift. Today, one of the big factors that can affect the accuracy of deployed models is if the data being used to generate predictions differs from data used to train the model.

Finally, Julien will share some cost optimization tips to help you make the most of your machine learning budget. Come and join us at AWS Sofia to learn more about AWS and SageMaker and meet more experts from the industry!