Re:Inventing the Media & Entertainment Industry using the Cloud

This session will cover the key trends that are shaping the Global and Indian Media & Entertainment (M&E) industry and how the cloud and AWS are playing a pivotal role in facilitating this given the agility, speed, innovation and cost effectiveness they are enabling.

Learn How AWS is Enabling the World's Most Advanced Media Workflows

AWS provides the building blocks for modern broadcast and OTT video workflows. In this session, we show how the broad array of AWS services can be used to build world class video workflows that are resilient, cost effective, and easy to manage. Both live and file-based video workflows are highlighted, and advanced monetization techniques are discussed.

Innovating with Content Distribution and Compute at the Edge

End users expect to be able to view static, dynamic, and streaming content anytime, anywhere, and on any device. Amazon Cloud Front is a web service that accelerates delivery of your websites, APIs, video content, or other web assets to end users around the globe with low latency, high data transfer speeds, and no commitments. In this session, learn what a content delivery network (CDN) such as Amazon CloudFront is and how it works, the benefits it provides, common challenges and needs, performance, recently released features and examples of how customers are using CloudFront. You will also learn about recustomizing content delivery through AWS Lambda@Edge - a server less compute service that lets you execute functions to customize the content delivered through Cloud Front.

Enabling Business Value in Media & Entertainment with Machine Learning

This session will cover enhancing common media workflows built around ingest, media asset management, live video, and OTT on-demand streaming and customer engagement with Machine Learning. It will cover a broad set of use cases ranging from meta data enrichment and content moderation to personalization including recommendations and how to address them using AWS's powerful portfolio of Machine learning services and partner offerings built using AWS services.

Re-imagining Data in Media Companies for a Digital World 

Video content providers are always looking for consumer insights into how their content is being consumer (who, when and where). This session gives an overview of how to build a central data repository to aggregate data from multiple sources to gain deep insights into customers and also increase agility by enabling real-time insights into their interests and activities.  This deeper insight and agility makes it possible to capitalize on new opportunities to engage audiences with targeted content based on content recommendation engines, as well as advertisements based on interests and demographics.

Data Lakes: An Enterprise’s Perspective

The Aditya Birla Group is one of the largest conglomerates in India and in the league of the Fortune 500. The Group has identified the need to streamline the collection, transformation, and presentation of data produced by the distributed units and systems, into a central data ecosystem. The Data & Analytics Organization at the Aditya Birla group addressed these requirements by creating a data lake with scalable analytics and query engines leveraging AWS services. In this session, ABG will outline their journey from a hindsight reporting focused company to an insights driven organization. They will cover solution architecture, challenges, and lessons learned from deploying a self-service insights platform. They will also walk through the design patterns they used and how they designed the solution to provide predictive analytics using AWS analytics and other services.

Demystifying Data Lakes 

To create the maximum value out the organization’s data landscape, traditional decision support system architecture are no longer adequate. New architectural patterns need to be developed to harness the power of data. To fully capture the value of using big data, organizations need to have flexible data architectures and able to extract maximum value from their data ecosystem. In this session, understand the conceptual construct and architectural pattern of a data lake including data ingestion at scale from a myriad of sources, storing data securely and durably and having the flexibility to process the data as required with the right platforms and tools to address a broad set of real-time and batch use cases. The session will also cover how leading global companies like FINRA, NASDAQ, Netflix and Rovio built and are leveraging their data lakes.

Data-warehouse Modernization with Amazon Redshift

Building and running a data warehouse—a central repository of information coming from one or more data sources—has always been complicated and expensive. Most data warehousing systems are complex to set up, cost millions of dollars in upfront software and hardware expenses, and can take months in planning, procurement, implementation, and deployment processes.  Amazon Redshift has changed how enterprises think about data warehousing by dramatically lowering the cost and effort associated with deploying data warehouse systems without compromising on features and performance. Amazon Redshift is a fast, fully managed, petabyte-scale data warehousing solution that makes it simple and cost-effective to analyze large volumes of data using existing business intelligence (BI) tools.

Real-Time Data Exploration for Application Monitoring and Log Analytics 

Learn how to address mission critical real-time use cases such as monitoring the performance of applications, web servers, and hardware and performing log and operational monitoring using Amazon Elastic search Service (AES). Elastic search is a fully featured search engine used for real-time analytics, and AES makes it easy to deploy Elastic search clusters on AWS. With AES, you can ingest and process billions of events per day, and explore the data using Kibana to discover patterns. In this session, we show you how to build an end-to-end analytics solution.

Panel Discussion: Big Data & Analytics Trends in India and leveraging the cloud to address the same 

Panel discussion on key Big Data and Analytics trends being witnessed across industries in India and how leading organizations are leveraging the cloud to address the same including challenges addressed, learnings and best practices.

Leveraging Machine Learning to Enhance Business Value

This session will cover how computer vision, natural language understanding, prediction, recommendation, robotics, and other machine learning models are being increasing used across industries including across Amazon business to enhance capabilities and improve customer experiences. We will also cover how to quickly leverage AWS managed machine learning services your business. We will also have 2 of our customers talk about how they have made use of some of these services to quickly and easily build machine learning powered features into their applications. Products and Services covered: Amazon Polly, Amazon Comprehend, Amazon Rekognition, Amazon Lex, Amazon Transcribe.

Welcome to the world of Video, Speech and Natural Languages

Products and Services: Deep Learning, Amazon Sagemaker, P3 instances.

Advent of Deep Learning has unlocked new opportunities of extracting insights from new type of data like video, speech and text and using them to add value to your business. In this session you learn how to build such deep learning models at scale using AWS provided GPUs, frameworks and managed platforms like Amazon SageMaker.

Intelligence of Things: IoT, AWS DeepLens, and Amazon SageMaker

With IoT, Machine Learning is going everywhere. Using Amazon SageMaker it's never been easier to build Intelligent Things. In this session we look at how we can push intelligence from cloud-trained models to the edge using AWS Greengrass and explore how devices such as AWS DeepLens make it easy to bring intelligence to your things.

Building a Recommender System on AWS

Be it recommending the right product to the customer, or the right movie to the viewer or the right news item to the reader, delivering personalized and relevant content to the end user is what all online providers are striving for in order to drive more traffic and better monetization. In this session, we will explore different techniques for implementing Recommender Systems, from simple metrics based suggestions to more complex solutions using Statistical Machine Learning and Deep Learning. We will dive into some public data sets and demonstrate how you can build a Recommender System using AWS services.

Supercharge Your Machine Learning Model with Amazon SageMaker

Machine learning often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and slow. Amazon SageMaker removes the complexity that holds back developer success with each of these steps. Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models. In this session you will learn how to use Amazon SageMaker to build, train, test, and deploy a machine learning model. We will use a real life use case to share the simplicity of building and deploying ML models on Amazon SageMaker.