The document discusses strategies for migrating databases to the cloud. It begins by outlining objectives and factors that contribute to successful database migration projects. It then covers various migration options like rehosting, replatforming, or rearchitecting databases. The document also discusses tools like the Schema Conversion Tool (SCT) and Database Migration Service (DMS) that can assist with assessment, conversion between database engines, and migrating data between on-premises and cloud databases. The overall process involves planning, assessment, executing the migration in phases, and testing.
The document contains various logos and graphics related to Amazon Web Services (AWS) products and services. It discusses computing services (EC2), databases (RDS), analytics (Elasticsearch, EMR), machine learning (SageMaker), serverless (Lambda), containers (ECS, EKS), storage (S3), security (IAM, WAF), developer tools (CodeStar, CodeBuild, CodeDeploy), and migration acceleration programs. The document appears to be marketing or sales material that promotes AWS offerings across various domains like compute, storage, databases, analytics, artificial intelligence, Internet of Things, and more.
The document discusses preparing teams for a cloud transformation. It recommends forming a Cloud Center of Excellence (CCOE) with cross-functional two-pizza sized teams focused on delivering quick wins. The CCOE should include product managers, architects, engineers focused on infrastructure, security, operations and applications. It also recommends starting with a minimum viable cloud to build capability before fully migrating workloads and optimizing for cloud native architectures over time. Training, certification and leadership commitment are key to support the transformation.
Supercell – Scaling Mobile Games (GAM301) - AWS re:Invent 2018Amazon Web Services
This document discusses Supercell's approach to scaling their mobile games and analytics infrastructure. Supercell has 5 games with hundreds of millions of active users. They use a microservices architecture and sharding to scale their games across thousands of EC2 instances. Their analytics pipeline collects terabytes of data daily, storing it in S3 and processing it with EMR. They have transitioned to separating compute and storage to better scale their analytics capabilities.
The document discusses strategies for migrating databases to the cloud. It begins by outlining objectives and factors that contribute to successful database migration projects. It then covers various migration options like rehosting, replatforming, or rearchitecting databases. The document also discusses tools like the Schema Conversion Tool (SCT) and Database Migration Service (DMS) that can assist with assessment, conversion between database engines, and migrating data between on-premises and cloud databases. The overall process involves planning, assessment, executing the migration in phases, and testing.
The document contains various logos and graphics related to Amazon Web Services (AWS) products and services. It discusses computing services (EC2), databases (RDS), analytics (Elasticsearch, EMR), machine learning (SageMaker), serverless (Lambda), containers (ECS, EKS), storage (S3), security (IAM, WAF), developer tools (CodeStar, CodeBuild, CodeDeploy), and migration acceleration programs. The document appears to be marketing or sales material that promotes AWS offerings across various domains like compute, storage, databases, analytics, artificial intelligence, Internet of Things, and more.
The document discusses preparing teams for a cloud transformation. It recommends forming a Cloud Center of Excellence (CCOE) with cross-functional two-pizza sized teams focused on delivering quick wins. The CCOE should include product managers, architects, engineers focused on infrastructure, security, operations and applications. It also recommends starting with a minimum viable cloud to build capability before fully migrating workloads and optimizing for cloud native architectures over time. Training, certification and leadership commitment are key to support the transformation.
Supercell – Scaling Mobile Games (GAM301) - AWS re:Invent 2018Amazon Web Services
This document discusses Supercell's approach to scaling their mobile games and analytics infrastructure. Supercell has 5 games with hundreds of millions of active users. They use a microservices architecture and sharding to scale their games across thousands of EC2 instances. Their analytics pipeline collects terabytes of data daily, storing it in S3 and processing it with EMR. They have transitioned to separating compute and storage to better scale their analytics capabilities.
Building an end to end image recognition service - Tel Aviv Summit 2018Amazon Web Services
In this session, we’ll learn how to build and deploy end to end solutions for ingesting and processing computer vision solutions, using machine learning models connected to live video streams, and getting insights such as face detection and object analysis. At the end of the session developers of all skill levels will be able to build their own deep learning powered, computer-vision applications. Attendees will learn how to experiment with different projects for face detection, object recognition and other video-based AWS Machine Learning services.
Amazon Cloud Directory Deep Dive (DAT364) - AWS re:Invent 2018Amazon Web Services
Learn how companies like FocusCura and Clouden have on-boarded hierarchical data at scale using Amazon Cloud Directory, a highly available, fully managed, serverless, hierarchical datastore. It’s well-suited for such use cases as human resources applications, course catalogs, device registry, network topology, and any application that needs fine-grained permissions (authorization). We do a deep dive into the internals of Cloud Directory and discuss best practices.
Trends in Digital Transformation (ARC212) - AWS re:Invent 2018Amazon Web Services
As industries digitally transform their existing business models to fend off competitors or disrupt new markets, they find their IT to be a limiting factor. In this session, we cover the trends of disruptions and opportunities of digital transformation, and the evolution of IT monoliths to microservices and now cloud native services. We also explore dependency management, or “lock in,” through a “choosing, using, and losing” mental model. Finally, we explore chaos architecture as an evolving method for exposing weaknesses before they become real problems.
Deep Dive on Amazon Rekognition, ft. Tinder & News UK (AIM307-R) - AWS re:Inv...Amazon Web Services
Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications.
[NEW LAUNCH!] How do I know I need a ledger database? An Introduction to Amaz...Amazon Web Services
Do you need a ledger database? Let's talk about the kinds of problems that Amazon Quantum Ledger Database (QLDB) can solve, and answer your questions about when and why you would use a ledger database. We'll showcase a presentation with benefits and use cases for Amazon QLDB.
AWS Compute Leadership Session: What’s New in Amazon EC2, Containers, and Ser...Amazon Web Services
Matt Garman, VP of AWS Compute Services, introduces the latest innovations in the compute space. In this keynote address, we announce new compute capabilities, and we share insights into what makes the AWS compute business unique. We also announce new capabilities for Amazon EC2 instances, EC2 networking, EC2 Spot Instances, Amazon Lightsail, Containers, and Serverless. Matt is joined by executives from our customers and partners who share valuable success stories of how Amazon EC2 has helped their journey to digital transformation.
Building Deep Learning Applications with TensorFlow and SageMaker on AWS - Te...Amazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. In this workshop, we provide an overview of deep learning, focusing on getting started with the TensorFlow framework on AWS.
Alexa, Where's My Car? A Test Drive of the AWS Connected Vehicle Solution (AM...Amazon Web Services
The transformation of the auto industry from manufacturers to mobility providers is centered on seamlessly and safely connecting vehicles to the outside world. In this session, we discuss how customers are using AWS for a variety of connected vehicle use cases. Leave this session with source code, architecture diagrams, and an understanding of how to use the AWS connected vehicle reference architecture to build your own prototypes. Also learn how companies leverage Amazon services such as Alexa, AWS IoT, AWS Greengrass, AWS Lambda, and Amazon Kinesis Data Analytics to rapidly develop and deploy innovative mobility services. Learn how to use new enhancements in your architectures, including the IoT Device Simulator, a scalable, simulated vehicle, load generation tool, as well as the AWS IoT Framework for Automotive Grade Linux (AGL), an integrated build tool for AGL that includes the AWS IoT Device SDK and AWS Greengrass.
[NEW LAUNCH!] Introducing Amazon Textract: Now in Preview (AIM363) - AWS re:I...Amazon Web Services
The document introduces Amazon Textract, a new AI service from Amazon for extracting text, tables, and other data from scanned documents. It discusses the need for processing large volumes of documents in industries like finance, insurance, and legal. The document outlines challenges with current document processing methods like manual entry and OCR. It then describes Amazon Textract's features for text, table, and form extraction and how it can simplify extracting structured data from documents without templates.
AI & ML at Amazon: AWS Developer Workshop - Web Summit 2018Amazon Web Services
AI & Machine Learning at Amazon: AWS Developer Workshop - Web Summit 2018
Amazon has been applying machine learning to create artifical intelligence features within its products and services for over 20 years. Join this session and learn about the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding, and how you can use easily accessible services from AWS to enable you to include AI features within your applications or build your own custom ML models for your own specific AI use cases.
Speaker: Ian Massingham - Director, Technical Evangelist, AWS
The document contains slides from a presentation on serverless architectures. It discusses several serverless patterns including building web apps and APIs with AWS Lambda, Amazon API Gateway and other services. It also covers stream processing patterns using Amazon Kinesis and analytics, real-time analytics examples, additional patterns like serverless data lakes and operations automation. The slides provide examples and best practices for implementing these serverless architectures and patterns on AWS.
0x32 Shades of #7f7f7f: The Tension Between Absolutes and Ambiguity in Securi...Amazon Web Services
The document discusses the tension between absolute security and allowing for ambiguity. It notes that security organizations aim to maximize customer value while minimizing costs over time. It also discusses concepts like entitlement, goal setting, and continually refining security approaches and questions to achieve the goal of zero vulnerabilities.
Training your engineers and developers the right way can increase the pace of adoption, cloud migration, and the delivery of business benefits. In this session, we discuss proven steps for training your technical teams so you can use the AWS Cloud securely, efficiently, and effectively. We also review structural mechanisms to help scale your organization's capacity to operate a cloud-based IT environment.
What's New in AR & VR: State of the World Report (ARV203) - AWS re:Invent 2018Amazon Web Services
In this session, Amazon Sumerian leaders provide an overview of the evolution of the augmented reality/virtual reality (AR/VR) industry. They discuss what has changed since Sumerian was announced at re:invent 2017. They also talk about key market trends, and they highlight areas with the highest adoption and potential. This is an immersive session, with examples and demonstrations of immersive experiences created using Amazon Sumerian.
Introduction to Serverless computing and AWS Lambda - Floor28Boaz Ziniman
Serverless computing allows you to build and run applications without the need for provisioning or managing servers. With Serverless computing, you can build web, mobile, and IoT backends; run stream processing or big data workloads; run chatbots, and more.
In this session, we will learn how to get started with Serverless computing using AWS Lambda, which lets you run code without provisioning or managing servers.
Simplify Your Front End Apps with Serverless Backend in the Cloud.Amazon Web Services
Customers are often looking at running their services at global scale, deploying applications to multiple regions. While it has traditionally been hard to do this, often requiring months of engineering work, serverless has changed the game!This hands-on talk will help you understand how to build two different versions of a multi-region, active-active serverless backend. Come learn the pros-and-cons of DNS routing versus IP Anycast, and see how you can leverage serverless services like Route 53, Global Accelerator, API Gateway, the Application Load Balancer, AWS Lambda and DynamoDB Global tables to build global scale, serverless applications.
Create an ML Factory in Financial Services with CI CD - FSI301 - New York AWS...Amazon Web Services
The document discusses creating a machine learning factory using AWS services. It describes combining Amazon SageMaker (for building, training, and deploying ML models) with Amazon CodeCommit, CodeBuild, and CodePipeline to create an automated pipeline. When model code or training data changes are committed to CodeCommit, CodePipeline will trigger CodeBuild to build a Docker image, train a model in SageMaker, and deploy the new model. This allows for continuous integration and deployment of ML models, improving the development process for highly-regulated industries like financial services.
Business Process Automation Using Crowdsourcing (AIM352) - AWS re:Invent 2018Amazon Web Services
While technology continues to improve, there are still many things that human beings can do much more effectively than computers, such as performing data deduplication or content moderation. Traditionally, such tasks have been accomplished by hiring a large temporary workforce—which is time consuming, expensive, and difficult to scale—or have gone undone. However, businesses or developers can use Amazon Mechanical Turk (Mechanical Turk) to access thousands of on-demand workers—and then integrate the results of that work directly into their business processes and systems. In this session, learn how enterprises are using Mechanical Turk to scale and automate their human-powered workflow.
How Different Large Organizations are Approaching Cloud AdoptionAmazon Web Services
The implementation of highly scalable, easy-to-deploy technology is transforming enterprises, but it’s not a one-size-fits-all approach. Organizations begin their cloud adoption journeys in many ways. Some start with pilot projects and others jump into mission-critical programs, but they are all starting with an existing infrastructure. Adopting cloud doesn’t mean scrapping it all and starting over. This session explores how organizations are using cloud while building on their existing technology and lessons they’ve learned along the way. In this session we will discuss when and how to leverage hybrid cloud computing to meet the needs of the enterprise. We will cover popular hybrid cloud use cases in enterprises, pillars to design a secure hybrid cloud environment and how to get started with AWS.
Get to Know Your Customers - Build and Innovate with a Modern Data ArchitectureAmazon Web Services
Your customers probably want a better experience with your brand. Your different business teams want and need better insights in their decision making. Almost certainly, your finance and operations teams require this to happen at a fraction of the cost of traditional on-premises options. Modern data architectures on AWS help many of our best customers realise all of those goals. Your business data contains critical information about customer behaviours, operational decisions, and many factors that have financial impact on your organisation. Increasingly, this data sits beyond your transactional systems, and is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed from our customers' requirements to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
Building an end to end image recognition service - Tel Aviv Summit 2018Amazon Web Services
In this session, we’ll learn how to build and deploy end to end solutions for ingesting and processing computer vision solutions, using machine learning models connected to live video streams, and getting insights such as face detection and object analysis. At the end of the session developers of all skill levels will be able to build their own deep learning powered, computer-vision applications. Attendees will learn how to experiment with different projects for face detection, object recognition and other video-based AWS Machine Learning services.
Amazon Cloud Directory Deep Dive (DAT364) - AWS re:Invent 2018Amazon Web Services
Learn how companies like FocusCura and Clouden have on-boarded hierarchical data at scale using Amazon Cloud Directory, a highly available, fully managed, serverless, hierarchical datastore. It’s well-suited for such use cases as human resources applications, course catalogs, device registry, network topology, and any application that needs fine-grained permissions (authorization). We do a deep dive into the internals of Cloud Directory and discuss best practices.
Trends in Digital Transformation (ARC212) - AWS re:Invent 2018Amazon Web Services
As industries digitally transform their existing business models to fend off competitors or disrupt new markets, they find their IT to be a limiting factor. In this session, we cover the trends of disruptions and opportunities of digital transformation, and the evolution of IT monoliths to microservices and now cloud native services. We also explore dependency management, or “lock in,” through a “choosing, using, and losing” mental model. Finally, we explore chaos architecture as an evolving method for exposing weaknesses before they become real problems.
Deep Dive on Amazon Rekognition, ft. Tinder & News UK (AIM307-R) - AWS re:Inv...Amazon Web Services
Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications.
[NEW LAUNCH!] How do I know I need a ledger database? An Introduction to Amaz...Amazon Web Services
Do you need a ledger database? Let's talk about the kinds of problems that Amazon Quantum Ledger Database (QLDB) can solve, and answer your questions about when and why you would use a ledger database. We'll showcase a presentation with benefits and use cases for Amazon QLDB.
AWS Compute Leadership Session: What’s New in Amazon EC2, Containers, and Ser...Amazon Web Services
Matt Garman, VP of AWS Compute Services, introduces the latest innovations in the compute space. In this keynote address, we announce new compute capabilities, and we share insights into what makes the AWS compute business unique. We also announce new capabilities for Amazon EC2 instances, EC2 networking, EC2 Spot Instances, Amazon Lightsail, Containers, and Serverless. Matt is joined by executives from our customers and partners who share valuable success stories of how Amazon EC2 has helped their journey to digital transformation.
Building Deep Learning Applications with TensorFlow and SageMaker on AWS - Te...Amazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. In this workshop, we provide an overview of deep learning, focusing on getting started with the TensorFlow framework on AWS.
Alexa, Where's My Car? A Test Drive of the AWS Connected Vehicle Solution (AM...Amazon Web Services
The transformation of the auto industry from manufacturers to mobility providers is centered on seamlessly and safely connecting vehicles to the outside world. In this session, we discuss how customers are using AWS for a variety of connected vehicle use cases. Leave this session with source code, architecture diagrams, and an understanding of how to use the AWS connected vehicle reference architecture to build your own prototypes. Also learn how companies leverage Amazon services such as Alexa, AWS IoT, AWS Greengrass, AWS Lambda, and Amazon Kinesis Data Analytics to rapidly develop and deploy innovative mobility services. Learn how to use new enhancements in your architectures, including the IoT Device Simulator, a scalable, simulated vehicle, load generation tool, as well as the AWS IoT Framework for Automotive Grade Linux (AGL), an integrated build tool for AGL that includes the AWS IoT Device SDK and AWS Greengrass.
[NEW LAUNCH!] Introducing Amazon Textract: Now in Preview (AIM363) - AWS re:I...Amazon Web Services
The document introduces Amazon Textract, a new AI service from Amazon for extracting text, tables, and other data from scanned documents. It discusses the need for processing large volumes of documents in industries like finance, insurance, and legal. The document outlines challenges with current document processing methods like manual entry and OCR. It then describes Amazon Textract's features for text, table, and form extraction and how it can simplify extracting structured data from documents without templates.
AI & ML at Amazon: AWS Developer Workshop - Web Summit 2018Amazon Web Services
AI & Machine Learning at Amazon: AWS Developer Workshop - Web Summit 2018
Amazon has been applying machine learning to create artifical intelligence features within its products and services for over 20 years. Join this session and learn about the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding, and how you can use easily accessible services from AWS to enable you to include AI features within your applications or build your own custom ML models for your own specific AI use cases.
Speaker: Ian Massingham - Director, Technical Evangelist, AWS
The document contains slides from a presentation on serverless architectures. It discusses several serverless patterns including building web apps and APIs with AWS Lambda, Amazon API Gateway and other services. It also covers stream processing patterns using Amazon Kinesis and analytics, real-time analytics examples, additional patterns like serverless data lakes and operations automation. The slides provide examples and best practices for implementing these serverless architectures and patterns on AWS.
0x32 Shades of #7f7f7f: The Tension Between Absolutes and Ambiguity in Securi...Amazon Web Services
The document discusses the tension between absolute security and allowing for ambiguity. It notes that security organizations aim to maximize customer value while minimizing costs over time. It also discusses concepts like entitlement, goal setting, and continually refining security approaches and questions to achieve the goal of zero vulnerabilities.
Training your engineers and developers the right way can increase the pace of adoption, cloud migration, and the delivery of business benefits. In this session, we discuss proven steps for training your technical teams so you can use the AWS Cloud securely, efficiently, and effectively. We also review structural mechanisms to help scale your organization's capacity to operate a cloud-based IT environment.
What's New in AR & VR: State of the World Report (ARV203) - AWS re:Invent 2018Amazon Web Services
In this session, Amazon Sumerian leaders provide an overview of the evolution of the augmented reality/virtual reality (AR/VR) industry. They discuss what has changed since Sumerian was announced at re:invent 2017. They also talk about key market trends, and they highlight areas with the highest adoption and potential. This is an immersive session, with examples and demonstrations of immersive experiences created using Amazon Sumerian.
Introduction to Serverless computing and AWS Lambda - Floor28Boaz Ziniman
Serverless computing allows you to build and run applications without the need for provisioning or managing servers. With Serverless computing, you can build web, mobile, and IoT backends; run stream processing or big data workloads; run chatbots, and more.
In this session, we will learn how to get started with Serverless computing using AWS Lambda, which lets you run code without provisioning or managing servers.
Simplify Your Front End Apps with Serverless Backend in the Cloud.Amazon Web Services
Customers are often looking at running their services at global scale, deploying applications to multiple regions. While it has traditionally been hard to do this, often requiring months of engineering work, serverless has changed the game!This hands-on talk will help you understand how to build two different versions of a multi-region, active-active serverless backend. Come learn the pros-and-cons of DNS routing versus IP Anycast, and see how you can leverage serverless services like Route 53, Global Accelerator, API Gateway, the Application Load Balancer, AWS Lambda and DynamoDB Global tables to build global scale, serverless applications.
Create an ML Factory in Financial Services with CI CD - FSI301 - New York AWS...Amazon Web Services
The document discusses creating a machine learning factory using AWS services. It describes combining Amazon SageMaker (for building, training, and deploying ML models) with Amazon CodeCommit, CodeBuild, and CodePipeline to create an automated pipeline. When model code or training data changes are committed to CodeCommit, CodePipeline will trigger CodeBuild to build a Docker image, train a model in SageMaker, and deploy the new model. This allows for continuous integration and deployment of ML models, improving the development process for highly-regulated industries like financial services.
Business Process Automation Using Crowdsourcing (AIM352) - AWS re:Invent 2018Amazon Web Services
While technology continues to improve, there are still many things that human beings can do much more effectively than computers, such as performing data deduplication or content moderation. Traditionally, such tasks have been accomplished by hiring a large temporary workforce—which is time consuming, expensive, and difficult to scale—or have gone undone. However, businesses or developers can use Amazon Mechanical Turk (Mechanical Turk) to access thousands of on-demand workers—and then integrate the results of that work directly into their business processes and systems. In this session, learn how enterprises are using Mechanical Turk to scale and automate their human-powered workflow.
How Different Large Organizations are Approaching Cloud AdoptionAmazon Web Services
The implementation of highly scalable, easy-to-deploy technology is transforming enterprises, but it’s not a one-size-fits-all approach. Organizations begin their cloud adoption journeys in many ways. Some start with pilot projects and others jump into mission-critical programs, but they are all starting with an existing infrastructure. Adopting cloud doesn’t mean scrapping it all and starting over. This session explores how organizations are using cloud while building on their existing technology and lessons they’ve learned along the way. In this session we will discuss when and how to leverage hybrid cloud computing to meet the needs of the enterprise. We will cover popular hybrid cloud use cases in enterprises, pillars to design a secure hybrid cloud environment and how to get started with AWS.
Get to Know Your Customers - Build and Innovate with a Modern Data ArchitectureAmazon Web Services
Your customers probably want a better experience with your brand. Your different business teams want and need better insights in their decision making. Almost certainly, your finance and operations teams require this to happen at a fraction of the cost of traditional on-premises options. Modern data architectures on AWS help many of our best customers realise all of those goals. Your business data contains critical information about customer behaviours, operational decisions, and many factors that have financial impact on your organisation. Increasingly, this data sits beyond your transactional systems, and is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed from our customers' requirements to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
The first step towards knowing your customer is to collect and extract insights and actionable information from your data. Learn how AWS enables you to cost efficiently store any amount of data and build an agile approach to data mining and visualization - helping you to make efficient business decisions and targeted offerings.
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.In this session, we will introduce the Data Lake concept and its implementation on AWS.We will explain the different roles our services play and how they fit into the Data Lake picture.
AWS Floor 28 - Building Data lake on AWSAdir Sharabi
AWS makes it easy to build and operate a highly scalable and flexible data platforms to collect, process, and analyze data so you can get timely insights and react quickly to new information. In this session we will talk about how to improve over time using your data. How do you take your everyday data and build relevant business insights, to help and continuously improve your business processes, and keep your innovation going based on your data.
Connecting the dots - How Amazon Neptune and Graph Databases can transform yo...Amazon Web Services
This document discusses Amazon Neptune, a fully managed graph database service. It provides an overview of graph databases and their advantages over traditional databases for modeling connected data. It then describes Amazon Neptune's key features, like automatic scaling, high availability across Availability Zones, integration with open standards like Gremlin and SPARQL, and ease of use on AWS. Examples are given showing how to model and query graph data using Gremlin and SPARQL. Finally, it discusses Amazon Neptune's architecture and roadmap for general availability later in 2018.
The first step towards knowing your customer is to collect and extract insights and actionable information from your data. Learn how AWS enables you to cost efficiently store any amount of data and build an agile approach to data mining and visualization - helping you to make efficient business decisions and targeted offerings.
Building a Data Lake for Your Enterprise, ft. Sysco (STG309) - AWS re:Invent ...Amazon Web Services
Data lakes are transforming the way enterprises store, analyze, and learn insights from their data. While data lakes are a relatively new concept, many enterprises have already generated significant business value from the insights gleaned. In this session, AWS experts and technology leaders from Sysco, a Fortune 50 company and leader in food distribution and marketing, explain why Sysco decided to evolve its data management capabilities to include data lakes and how they customized them to support diverse querying capabilities and data science use cases. They also discuss how to architect different aspects of a data lake—ingestion from disparate sources, data consumption, and usability layers—and how to track data ingestion and consumption, monitor associated costs, enforce wanted levels of user access, manage data file formats, synchronize production and non-production environments, and maintain data integrity. Services to be discussed include Amazon S3 and S3 Select, Amazon Athena, Amazon EMR, Amazon EC2, and Amazon Redshift Spectrum.
AI/ML with Data Lakes: Counterintuitive Consumer Insights in Retail (RET206) ...Amazon Web Services
In this session, learn how data scientists in the retail industry, from companies like Tapestry, Coach, and Kate Spade, are finding new, counterintuitive consumer insights using AWS artificial intelligence services in a data lake. By leveraging data from various retail systems, including CRM, marketing, e-commerce, point of sale, order management, merchandising, and customer care, we show you how these consumer insights might influence new and interesting retail use cases while establishing a data-driven culture within the organization. Services referenced include Amazon S3, Amazon Machine Learning, Amazon QuickSight, Amazon SageMaker, among others.
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...Amazon Web Services
Most companies are overrun with data, yet they lack critical insights to make timely and accurate business decisions. They are missing the opportunity to combine large amounts of new, unstructured big data that resides outside their data warehouse with trusted, structured data inside their data warehouse. In this session, we discuss the most common use cases with Amazon Redshift, and we take an in-depth look at how modern data warehousing blends and analyzes all your data to give you deeper insights to run your business. Equinox Fitness Clubs joins us to share their journey from static reports, redundant data, and inefficient data intergration to a modern and flexible data lake and data warehouse architecture that delivers dynamic reports based on trusted data.
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...Amazon Web Services
The document discusses building a data lake using Amazon S3 and Amazon Glacier for storage. It covers topics like what is big data, what is a data lake, achievable business outcomes from a data lake, securing the data lake, and examples of what can be done with analytics services on AWS. The presentation provides examples of using services like Amazon Comprehend, Amazon Transcribe, Kinesis, Athena and QuickSight for natural language processing, audio analysis, real-time streaming and visualization.
Social Media Analytics with Amazon QuickSight (ANT370) - AWS re:Invent 2018Amazon Web Services
Realizing the value of social media analytics can bolster your business goals. This type of analysis has grown in recent years due to the large amount of available information and the speed at which it can be collected and analyzed. In this workshop, we build a serverless data processing and machine learning (ML) pipeline that provides a multi-lingual social media dashboard of tweets within Amazon QuickSight. We leverage API-driven ML services, AWS Glue, Amazon Athena and Amazon QuickSight. These building blocks are put together with very little code by leveraging serverless offerings within AWS.
This document discusses big data and machine learning. It begins by defining big data using the 5 V's: volume, velocity, variety, veracity, and value. It then discusses challenges organizations face with big data, including which tools to use and determining what data they have. The remainder discusses how to gain business value from data through architectures like data lakes, analytics, and machine learning services on AWS. It provides an example of how Netflix evolved its data pipeline and emphasizes agility. Finally, it discusses how machine learning relies on big data and new tools are needed for data scientists.
A decade ago, relational databases were used for nearly every use case. Today, new technologies are enabling a revolution in databases, creating new options for document, key: value, in-memory, search, and graph capabilities that do not use relational tables. We’ll discuss this revolution in database options and who is using them.
Level: 200
Speaker: Samir Karande - Sr. Manager, Solutions Architecture, AWS
Database Week at the San Francicso Loft
Non-Relational Revolution
A decade ago, relational databases were used for nearly every use case. Today, new technologies are enabling a revolution in databases, creating new options for document, key: value, in-memory, search, and graph capabilities that do not use relational tables. We’ll discuss this revolution in database options and who is using them.
Level: 200
Speakers:
Smitty Weygant - Solutions Architect, AWS
Karan Desai - Solutions Architect, AWS
SaaS Analytics and Metrics: Capturing and Surfacing the Data That's Fundament...Amazon Web Services
Metrics are fundamental to succeeding with SaaS. As you pour tenants into a shared infrastructure environment, you need to rely on a rich collection of data that can drive insights into the architectural, operational, and business dimensions of your SaaS solution. In this session, learn to identify the different types of metrics commonly collected by SaaS providers and connect these with the design and architecture strategies that are employed to surface and analyze this data on AWS. We look at how SaaS organizations instrument, aggregate, publish and build actionable views of this data with specific emphasis on how a robust metrics architecture can fundamentally impact the operational, architectural, and business decision-making process for SaaS organizations.
Build, train, and deploy machine learning models at scale
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 too slow.
Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models.
Olivier Bergeret - AWS
https://dataxday.fr/
Video available: https://www.youtube.com/watch?v=3eV4x_GR_f8
Artificial Intelligence nella realtà di oggi: come utilizzarla al meglioAmazon Web Services
L'intelligenza Artificiale è qui questa volta, per restare. Per le aziende, l'intelligenza artificiale si concretizza in soluzioni che migliorano l'esperienza dei clienti ottimizzando, automatizzando e personalizzando attività ad alto volume e riducendo al contempo costi e tempi, accelerando notevolmente il ritmo di innovazione. In questa sessione, approfondiremo i servizi AI di AWS che promuovo l'innovazione in azienda mantenendo la conformità con diversi regimi come HIPAA, PCI e altro. Infine, presenteremo le architetture AWS necessarie per supportare i carichi di lavoro di apprendimento automatico e deep learning.
This document discusses how a company called EarEcstasy modernized their data architecture to enable better business insights and customer experiences. It describes their journey from a traditional B2B model to launching smart earbuds directly to consumers. This required answering new types of questions quickly, so EarEcstasy looked to build a modern data architecture on AWS. The summary outlines three key outcomes: 1) Modernizing and consolidating their data infrastructure, 2) Innovating for new revenues through personalization, and 3) Enabling real-time customer engagement.
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Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
1) The document discusses building a minimum viable product (MVP) using Amazon Web Services (AWS).
2) It provides an example of an MVP for an omni-channel messenger platform that was built from 2017 to connect ecommerce stores to customers via web chat, Facebook Messenger, WhatsApp, and other channels.
3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.
This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.
This document discusses building serverless web applications using AWS services like API Gateway, Lambda, DynamoDB, S3 and Amplify. It provides an overview of each service and how they can work together to create a scalable, secure and cost-effective serverless application stack without having to manage servers or infrastructure. Key services covered include API Gateway for hosting APIs, Lambda for backend logic, DynamoDB for database needs, S3 for static content, and Amplify for frontend hosting and continuous deployment.
This document provides tips for fundraising from startup founders Roland Yau and Sze Lok Chan. It discusses generating competition to create urgency for investors, fundraising in parallel rather than sequentially, having a clear fundraising narrative focused on what you do and why it's compelling, and prioritizing relationships with people over firms. It also notes how the pandemic has changed fundraising, with examples of deals done virtually during this time. The tips emphasize being fully prepared before fundraising and cultivating connections with investors in advance.
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.