Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Creating a serverless real-time analytics platform powered by machine learning in the cloud

Roy Ben-Alta (Amazon Web Services), Allan MacInnis (Amazon Web Services)
2:55pm3:35pm Wednesday, September 27, 2017
Data Engineering & Architecture, Stream processing and analytics
Location: 1A 15/16/17 Level: Intermediate
Average rating: ****.
(4.33, 3 ratings)

Who is this presentation for?

  • Data engineers, software engineers, and data architects

Prerequisite knowledge

  • A basic understanding of stream processing, machine learning, Kafka, SQL, Spark, Python, Java, and Hadoop

What you'll learn

  • Discover AWS solutions for cloud-native machine learning and deep learning technologies
  • Explore a serverless real-time analytics system using AWS Lambda, Amazon Rekognition, Amazon Kinesis Analytics, and Amazon DynamoDB

Description

Speed matters. Today, decisions are made based on real-time insights, but in order to support the substantial growth of streaming data, companies are required to innovate. Roy Ben-Alta and Allan MacInnis explore AWS solutions powered by machine learning and artificial intelligence.

AWS offers a family of AI services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. Amazon AI services bring natural language understanding (NLU), automatic speech recognition (ASR), visual search and image recognition, text-to-speech (TTS), and machine learning (ML) technologies within reach of every developer. And Kinesis Streams is a foundational service which over a dozen AWS and Amazon retail services use to capture and process streaming data.

Roy and Allan offer an overview of Amazon Kinesis and AWS AI and share a live interactive demo of a serverless real-time analytics system using AWS Lambda, Amazon Rekognition, Amazon Kinesis Analytics, and Amazon DynamoDB. Along the way, Roy and Allan highlight key architectural features—covering how Amazon extended data analytics and machine learning algorithms, such as anomaly detection, to operate over streaming data, as well as trends Amazon is seeing in the use of stream data processing.

Topics include:

  • Stream processing
  • Streaming analytics
  • Image recognition
  • Architectural patterns and best practices
  • Machine learning over streaming data
  • Serverless architecture
Photo of Roy Ben-Alta

Roy Ben-Alta

Amazon Web Services

Roy Ben-Alta is a solution architect and principal business development manager at Amazon Web Services, where he focuses on AI and real-time streaming technologies and working with AWS customers to build data-driven products (whether batch or real time) and create solutions powered by ML in the cloud. Roy has worked in the data and analytics industry for over a decade and has helped hundreds of customers bring compelling data-driven products to the market. He serves on the advisory board of Applied Mathematics and Data Science at Post University in Connecticut. Roy holds a BSc in information systems and an MBA from the University of Georgia.

Photo of Allan MacInnis

Allan MacInnis

Amazon Web Services

Allan MacInnis is a solutions architect at Amazon Web Services, where he works on streaming data and analytics and helps AWS customers build solutions that enable them to gain immediate insight into their business and operations. Allan has held a number of roles at Amazon, including software development manager, where he helped to build innovative new products such as the Amazon Kindle and Amazon Flex. Previously, he spent several years as a software developer and architect at Dell. Allan holds a degree in electrical engineering from Dalhousie University.