aws machine learning
Within machine learning, the hardest aspect often becomes deploying to production, until the time comes to address the issue. Applied at scale, this issue can hinder deployment, and at the worst, kill the project entirely. In this video, we will…
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…
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…
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…
Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. In this session, we provide an overview for one of…
Using data in your data warehouse for machine learning use cases like churn prediction can be complicated because of the different tools and skills required. In this session, learn how with Amazon Redshift Machine Learning, you can use SQL to…