AWS Sagemaker
Amazon SageMaker is a cloud machine-learning platform that was launched in November 2017. SageMaker enables developers to create, train, and deploy machine-learning models in the cloud. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices.
For more than two decades, Amazon has been fighting fraud across all its online businesses, from the merchant side, with Amazon.com and subsidiary businesses like Zappos, to AWS digital services. This breadth of experience fighting online fraud includes payments fraud,…
Customer service conversations typically revolve around one or more topics and contain related questions. Answering these questions seamlessly is essential for a good conversational experience. In this session, learn how you can build an intelligent bot with Amazon Lex and…
As the ability to deliver more sophisticated digital experiences evolve over time, the expectation and demand from customers to receive a more personalized experience from companies and products they engage with have also increased. Customers today expect real-time, curated experiences…
Organizations have millions of physical documents and forms that hold critical business data. These documents, such as insurance claims or loan applications, have structured and unstructured data that are either extracted by humans or by rule-based systems which are not…
Your contact center is the biggest touchpoint between you and your customers, and every engagement can provide your team with powerful insights. In this session, we show how to leverage the new capabilities in Amazon Connect such as Contact Lens…
Cloud security at AWS is the highest priority. At AWS, building a secure environment from our data centers to our network architecture is of paramount importance. The same principles apply to machine learning where we provide a secure environment using…
AWS offers and delivers the broadest choice of powerful compute, high speed networking, and scalable high-performance storage options for any machine learning (ML) project or application. You can also choose the ML infrastructure to implement a fully managed ML Deployment…
Machine learned models and data-driven systems are being increasingly used to help make decisions in application domains such as financial services, healthcare, education, and human resources. With the goal that a significant portion of these decision systems becoming fully-automated, there…
Preparing training data is a critical step in machine learning. Preparing data involves creating labelled data, creating features, visualizing the features, and processing the data so it can be made available for training. In this session, learn how to use…
