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This AWS Machine Learning & AWS Deep Learning tutorial shows how powerful functions as a service are and how easy it is to get up and running with them.

Learning Objectives:
# Learn AWS Machine Learning with a demo.
# Become aquainted with the popular algorithms provided with Amazon Machine Learning.
# Learn how to use algorithms for training in Amazon Deep Learning
# Learn how the algorithms in Amazon Deep Learning were architected to be faster and more efficient by design”
# AWS Deep Learning Deep Dive.
# Checking Deep Learning logs.
# Understanding AWS Deep Learning pricing.
# Learn about the breadth of AI services available on the AWS Cloud.
# Gain insight into AWS API Services: Amazon Rekognition, Amazon Comprehend, Amazon Polly, Amazon Lex, Amazon Transcribe, Amazon Translate.
# Learn about AWS machine learning platform Amazon SageMaker, and supported frameworks such as TensorFlow, Apache MXNet and PyTorch.

Artificial intelligence is powering unprecedented innovation across industries – from healthcare to automotive to finance. However, many organizations still struggle with the best approach to data and tools to build intelligent systems. In this session, you’ll learn how to build powerful AI applications on AWS using computer vision and language services, as well as Amazon SageMaker, a fully-managed platform for machine learning that allows you to quickly and easily build, train, and deploy machine learning models at any scale. Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS.

Machine learning (ML) is having a major impact on society, so how can companies accelerate innovation and quickly drive their developers and data scientists to adopt ML? In this session, learn how customers are leveraging Amazon ML services, from API-driven machine intelligence to the most complex ML solutions requiring the latest NVidia GPU processors. We also describe how you can easily apply computer vision and natural language services to existing or new applications, and we show you how to automate ML pipelines with platform services like Amazon SageMaker.

To build the next set of personalized and engaging applications, more and more developers are adding ML to their applications. In this session, you’ll learn the basics behind machine learning and deep learning, and you’ll walk out with all the things you need to build an image classifier for your application.


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