IoT can give you great insight into consumer behaviour and demand, helping to you create the innovative, revenue-generating services of the future. However, there are still lots of challenges around collecting data from devices, which often have significant limitations in terms of processing power, memory and interfaces.

In this presentation, Danilo talks about how Amazon AI services can be used to augment device capabilities to make data collection, storage and analytics easier. He also considers how people can start interacting with machines in a more natural way, for example using natural language understanding (NLU), automatic speech recognition (ASR), visual search and image recognition, text-to-speech (TTS).

Learning objectives:

  • Learn how to design IoT solutions using services such as AWS Greengrass and AWS IoT
  • Gain insights into practical use cases for Amazon AI services
  • Understand the possibilities of using AI from an IoT device

You have already started connecting your devices to AWS IoT. You can control them from the cloud. And you can collect, store and analyse data from all your devices in the cloud. So far so good, but you now need to build an architecture that will serve millions of users and devices concurrently.

In this session, Jan will explain how you can build a real world IoT architecture that serves millions of devices. The talk will focus on user and device onboarding, device and user access management, message exchange and end user access to live and historical data stored in the cloud.

Learning objectives:

  • Learn simple steps to build a real-world IoT architecture that serves millions of devices
  • Understand how to onboard and manage users and IoT devices and to access live and historial data in the cloud

If you knew the state of everything in the world, and could apply logic on top of the data, what problems could you solve?

AWS IoT Services help you collect and send data to the cloud, make it easy to load and analyse that information, and provide the ability to manage your devices, so you can focus on developing applications that fit your needs.

Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualisation tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.

In this webinar, Neeraj explains how you can use AWS IoT and Amazon Machine Learning together to build smart IoT applications. We will demonstrate how to setup Amazon Machine Learning, create and train Machine Learning models for your applications. We will then use these models in our IoT Applications, in real time.

Learning objectives:

  • Understand why you may use Amazon Machine Learning with IoT and how to set it up.
  • Understand how to use IoT Rules Engine

Artificial Intelligence (AI) is enhancing many of the services that we interact with today. It can improve the customer experience of many services to make them more accessible, whilst providing information faster in a format that feels more natural.

AWS provides a collection of highly scalable, pre-trained and pre-tuned managed AI services that you can adopt without any previous artificial intelligence or deep learning knowledge. In this webinar, Steve explains how to implement each of these services to improve the user journey for a flight booking and check-in system.

The AWS solutions discussed here include Amazon Polly, which provides audio instructions for sight-impaired users and Amazon Rekognition, which provides an additional layer of security during the check-in process, matching users with customer data on file. Finally, Amazon Lex is used to enable customers to make future flight bookings using only their voices.

Learning objectives:

  • Understand why you may wish to use AI in your applications today
  • Identify the common AI challenges and practical use cases for Amazon AI services
  • Implement Amazon AI services without a PhD or Data Science background

Scaling IoT platforms to support production workloads can be difficult and time consuming. In this webinar, we’ll see how to design real-world IoT applications with a serverless back end. This kind of architecture can grow from very few to virtually unlimited users without any infrastructure or servers to manage. In particular, we’ll see an example of data collection from multiple IoT sensors, and how to process streaming data and present the results in a consolidated web dashboard.

Learning objectives:

  • Learn how to implement data collection from IoT sensors using service such as AWS IoT
  • Leverage the Amazon Kinesis platform to analyse streaming data
  • Understand best practices to design a serverless architecture to process and consolidate results