by Nader Dabit, Developer Advocate AWS
You’ve got an awesome startup idea – Wild Rydes! The next generation in transportation will be driven by a willing unicorn population and your new startup will produce the worlds first unicorn hailing services. It’s just seven days to launch, and your designers have delivered the final designs for your website, but your idea depends on the mobile economy! Can you build out your web and mobile infrastructure in time for your launch?
Across three days, AWS experts will guide you through all the pieces that are needed to produce an awesome mobile experience for both your unicorns and your riders.
by Brice Pelle, Enterprise Support Lead, AWS
One of the features of your website is a user sign-up form. Users can sign-up for a mailing list, but it doesn’t do anything! The designers left out the functionality. In this session, we will fix that and allow people to sign-up using Amazon Pinpoint. We’ll introduce the AWS SDKs for JavaScript and how they can help.
by John Burry, Principal Mobile Solutions Architect, AWS
The web application is almost complete, but we need capabilities to allow our users to request a ride on a unicorn. In this session, we will take a look at how we can implement an API in the cloud that can scale with your startup.
by Brice Pelle, Enterprise Support Lead, AWS
Production mobile apps don’t get built on a developer workstation. In this session, you will learn what AWS offers to turn your development process into a well-maintained machine. Using continuous integration, your builds will be consistent and documented. We’ll show you how to automatically build a releasable package with AWS Code* services.
by Nader Dabit, Developer Advocate AWS
You’ve got an awesome startup idea – Wild Rydes! The next generation in transportation will be driven by a willing unicorn population and your new startup will produce the worlds first unicorn hailing services. It’s just seven days to launch, and your designers have delivered the final designs for your website, but your idea depends on the mobile economy! Can you build out your web and mobile infrastructure in time for your launch?
Across three days, AWS experts will guide you through all the pieces that are needed to produce an awesome mobile experience for both your unicorns and your riders.
This document discusses machine learning and Amazon Web Services' ML products and services. It covers AWS's ML infrastructure, AI services like Amazon Rekognition, efforts to improve training and inference costs through new instance types and Amazon Elastic Inference, and making it easier for developers to obtain labeled data through Amazon SageMaker. The document emphasizes that AWS has more ML customers and services than any other provider and is focused on increasing ease of use, reducing costs, and improving data preparation for ML developers.
by Richard Threlkeld, Sr. Product Manager, AWS
We introduced quite a few mobile backend services in Day 1. In this session, we’ll re-visit those backend services (Amazon Pinpoint, Amazon Cognito and AWS Lambda) and show you how to implement the client side of those services with React Native.
by Dennis Hills, Developer Advocate, AWS
Start off mobile week with an introduction to what you need to know to develop engaging web and mobile applications using AWS client libraries, open source projects and services. Learn how easy it is to build a web and mobile backend for your ideas.
by Nader Dabit, Developer Advocate AWS
You’ve got an awesome startup idea – Wild Rydes! The next generation in transportation will be driven by a willing unicorn population and your new startup will produce the worlds first unicorn hailing services. It’s just seven days to launch, and your designers have delivered the final designs for your website, but your idea depends on the mobile economy! Can you build out your web and mobile infrastructure in time for your launch?
Across three days, AWS experts will guide you through all the pieces that are needed to produce an awesome mobile experience for both your unicorns and your riders.
by Brice Pelle, Enterprise Support Lead, AWS
One of the features of your website is a user sign-up form. Users can sign-up for a mailing list, but it doesn’t do anything! The designers left out the functionality. In this session, we will fix that and allow people to sign-up using Amazon Pinpoint. We’ll introduce the AWS SDKs for JavaScript and how they can help.
by John Burry, Principal Mobile Solutions Architect, AWS
The web application is almost complete, but we need capabilities to allow our users to request a ride on a unicorn. In this session, we will take a look at how we can implement an API in the cloud that can scale with your startup.
by Brice Pelle, Enterprise Support Lead, AWS
Production mobile apps don’t get built on a developer workstation. In this session, you will learn what AWS offers to turn your development process into a well-maintained machine. Using continuous integration, your builds will be consistent and documented. We’ll show you how to automatically build a releasable package with AWS Code* services.
by Nader Dabit, Developer Advocate AWS
You’ve got an awesome startup idea – Wild Rydes! The next generation in transportation will be driven by a willing unicorn population and your new startup will produce the worlds first unicorn hailing services. It’s just seven days to launch, and your designers have delivered the final designs for your website, but your idea depends on the mobile economy! Can you build out your web and mobile infrastructure in time for your launch?
Across three days, AWS experts will guide you through all the pieces that are needed to produce an awesome mobile experience for both your unicorns and your riders.
This document discusses machine learning and Amazon Web Services' ML products and services. It covers AWS's ML infrastructure, AI services like Amazon Rekognition, efforts to improve training and inference costs through new instance types and Amazon Elastic Inference, and making it easier for developers to obtain labeled data through Amazon SageMaker. The document emphasizes that AWS has more ML customers and services than any other provider and is focused on increasing ease of use, reducing costs, and improving data preparation for ML developers.
by Richard Threlkeld, Sr. Product Manager, AWS
We introduced quite a few mobile backend services in Day 1. In this session, we’ll re-visit those backend services (Amazon Pinpoint, Amazon Cognito and AWS Lambda) and show you how to implement the client side of those services with React Native.
by Dennis Hills, Developer Advocate, AWS
Start off mobile week with an introduction to what you need to know to develop engaging web and mobile applications using AWS client libraries, open source projects and services. Learn how easy it is to build a web and mobile backend for your ideas.
The document discusses building mobile apps using AWS cloud services. It provides an agenda for a mobile development workshop covering web, React Native, and data-driven app development. It lists prerequisites for each day including knowledge of JavaScript, Node.js, and mobile app frameworks. It also summarizes trends in mobile use and apps, and strategies for making apps stand out. Finally, it outlines AWS services for mobile and how the AWS Mobile SDK and Mobile Hub can help integrate AWS in mobile apps.
The document discusses serverless computing and provides an overview of serverless technologies. It begins with an example of how a developer can build an application without servers using AWS serverless services like API Gateway, Lambda, DynamoDB, and S3. It then discusses serverless benefits like automatic scaling, pay per use, and easier management. The rest of the document demonstrates how to develop, deploy, customize and connect serverless applications, including using AWS services for IDEs, CI/CD pipelines, logging, and connecting to enterprise systems and databases. It also covers GraphQL and AWS AppSync for building real-time data APIs on serverless.
SRV326 Build a Voice-based Chatbot for Your Amazon Connect Contact CenterAmazon Web Services
In this workshop, you learn how easy it is to incorporate a voice-based Amazon Lex chatbot into your Amazon Connect contact center. We walk you through configuring your own Amazon Connect contact center, implementing a chatbot, and using it in your workflows to deliver a personalized voice-based caller experience. You also learn to further personalize caller experiences by using AWS Lambda to access caller information from your customer data system. Leave the session with a functioning Amazon Connect contact center and a voice-enabled chatbot that you can continually modify to your business needs over time. Come prepared to build by bringing your laptop and a phone to make test calls.
by Nader Dabit, Developer Advocate AWS
You’ve got an awesome startup idea – Wild Rydes! The next generation in transportation will be driven by a willing unicorn population and your new startup will produce the worlds first unicorn hailing services. It’s just seven days to launch, and your designers have delivered the final designs for your website, but your idea depends on the mobile economy! Can you build out your web and mobile infrastructure in time for your launch?
Across three days, AWS experts will guide you through all the pieces that are needed to produce an awesome mobile experience for both your unicorns and your riders.
by John Pignata, Startup Solutions Architect, AWS
In this workshop you’ll get a crash course in building and deploying your first serverless application running on AWS Lambda and Amazon API Gateway. You’ll learn to use development tools such as AWS Cloud9, AWS CodeStar, and AWS Serverless Application Model (SAM) to author, deploy, and debug your application. Requirements: laptop, text editor, AWS account, and AWS Command Line Interface (CLI) installed and configured.
by Karthik Saligrama, SDE AWS AppSync
You’ve got an awesome startup idea – Wild Rydes! The next generation in transportation will be driven by a willing unicorn population and your new startup will produce the worlds first unicorn hailing services. It’s just seven days to launch, and your designers have delivered the final designs for your website, but your idea depends on the mobile economy! Can you build out your web and mobile infrastructure in time for your launch?
Across three days, AWS experts will guide you through all the pieces that are needed to produce an awesome mobile experience for both your unicorns and your riders.
Developers need to quickly develop, build, and deploy web applications. In this session, we show you how AWS CodeStar makes it easy for you to set up a continuous delivery toolchain and start developing on AWS in minutes. We also share best practices for managing and deploying web applications using AWS Elastic Beanstalk.
Len Henry
Sr. Solutions Architect, AWS
by Brice Pelle, Enterprise Support Lead, AWS
The designers have included a custom UI for a sign-in and sign-up page, but they forgot to actually include a service sign-up. In this session, we’ll wire up the sign-up and sign-in process with Amazon Cognito and link it to Amazon Pinpoint so you can run campaigns in the future to engage your users.
This document discusses Amazon Web Services' container services and options for running containers on AWS. It provides an overview of containers and why they are popular, such as being portable, lightweight, and easy to deploy. It then describes Amazon's main container services: Amazon Elastic Container Service (ECS) for managing Docker containers, Amazon Elastic Container Service for Kubernetes (EKS) for managing Kubernetes clusters, and AWS Fargate for launching containers without needing to manage infrastructure. It explains that these options provide customers flexibility in how they can run containers on AWS based on their specific needs and workflows.
The document discusses Amazon Web Services (AWS) and provides an overview of its services and capabilities for developers. It notes that AWS allows for quick provisioning of resources without having to rebuild infrastructure, and that it offers a broad and fully-featured platform. The document also lists upcoming presentations at Collision about using AWS for containers, serverless applications, machine learning/AI, and mobile development.
This document discusses infrastructure as code using the AWS Cloud Development Kit (CDK). It begins by describing manual infrastructure creation and then imperative and declarative infrastructure as code approaches. It introduces the CDK, which allows defining infrastructure in familiar programming languages like JavaScript. With the CDK, constructs can be used to provision many underlying AWS resources with a single class, making infrastructure definition more abstract and code-like.
Heeki Park, a senior consultant at AWS, gives a presentation on serverless computing using AWS Lambda. The presentation introduces serverless concepts, demonstrates common use cases for AWS Lambda including web applications, data processing, and IT automation. It also demonstrates how to use AWS Lambda, and how it integrates with other AWS services like API Gateway, Step Functions, Lex and others.
The document discusses AWS Cloud9, a cloud-based integrated development environment (IDE) tool. It was created by AWS to address issues developers face like relying on local machines, the cumbersome setup of development environments, difficulties collaborating on code, and limitations of existing IDEs for serverless applications. AWS Cloud9 allows developers to code in any browser, quickly create new development environments matched to production, and collaboratively code in real time with direct access to AWS services. It is available in several regions and has no charge for using the IDE itself.
The AWS Amplify CLI and tools help developers build full-stack applications by automatically configuring and connecting AWS cloud services like databases, APIs, authentication, analytics, hosting and storage. The CLI creates and manages these services, while the JavaScript library connects front-end apps to the cloud services. Supported frameworks include React, Angular, Vue and Ionic.
This document provides an overview of serverless development using AWS Lambda. It discusses common use cases for serverless applications including web apps, data processing, chatbots, backends, and IT automation. It also covers topics like pricing, resource allocation, available event sources and services, and development tools. The document contains code samples and screenshots related to building serverless applications on AWS Lambda.
Authentication and Identity with Amazon Cognito & Analytics with Amazon PinpointAmazon Web Services
by Adrian Hall, Sr. Developer Advocate, AWS
One of the key challenges for mobile applications is managing users and their identities in order to support monetization strategies, provide differentiated services, and manage fine grained access and data controls. In this session, you’ll learn how Amazon Cognito provides user sign-up and sign-in as part of your onboarding workflow and advanced capabilities for data access/feature management and security.
Once you’ve acquired a user, then the even more difficult task of retaining and monetizing the user begins. In order to retain the user, they must be engaged with the application. In this session, you’ll learn how to use Amazon Pinpoint to implement user engagement strategies via data and event capture, advanced analytics, integrated multi-channel messaging (mobile push, SMS, email) to drive campaigns (both transactional and targeted) based on demographics and audience segmentation.
Build a Voice-Based Chatbot for Your Amazon Connect Contact CenterAmazon Web Services
Learn how easy it is to incorporate a voice-based Amazon Lex chatbot into your Amazon Connect contact center. In this workshop, we walk you through configuring your own Amazon Connect contact center, implementing a chatbot, and using it in your workflows to deliver a personalized voice-based caller experience. You also have the opportunity to further personalize caller experiences by using AWS Lambda to access caller information from your customer data system. Leave this workshop with a functioning Amazon Connect contact center and a voice-enabled chatbot that you can continually modify to your business needs over time. Come prepared to build by bringing your laptop and a phone to make test calls.
by Nader Dabit, Developer Advocate AWS
You’ve got an awesome startup idea – Wild Rydes! The next generation in transportation will be driven by a willing unicorn population and your new startup will produce the worlds first unicorn hailing services. It’s just seven days to launch, and your designers have delivered the final designs for your website, but your idea depends on the mobile economy! Can you build out your web and mobile infrastructure in time for your launch?
Across three days, AWS experts will guide you through all the pieces that are needed to produce an awesome mobile experience for both your unicorns and your riders.
WhereML a Serverless ML Powered Location Guessing Twitter BotRandall Hunt
Learn how we designed, built, and deployed the @WhereML Twitter bot that can identify where in the world a picture was taken using only the pixels in the image. We'll dive deep on artificial intelligence and deep learning with the MXNet framework and also talk about working with the Twitter Account Activity API. The bot is entirely autoscaling and powered by Amazon API Gateway and AWS Lambda which means, as a customer, you don't manage any infrastructure. Finally we'll close with a discussion around custom authorizers in API Gateway and when to use them.
NEW LAUNCH! Integrating Amazon SageMaker into your Enterprise - MCL345 - re:I...Amazon Web Services
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walkthrough the machine learning lifecycle in an integrated AWS environment and discuss best practices.Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
The document discusses building mobile apps using AWS cloud services. It provides an agenda for a mobile development workshop covering web, React Native, and data-driven app development. It lists prerequisites for each day including knowledge of JavaScript, Node.js, and mobile app frameworks. It also summarizes trends in mobile use and apps, and strategies for making apps stand out. Finally, it outlines AWS services for mobile and how the AWS Mobile SDK and Mobile Hub can help integrate AWS in mobile apps.
The document discusses serverless computing and provides an overview of serverless technologies. It begins with an example of how a developer can build an application without servers using AWS serverless services like API Gateway, Lambda, DynamoDB, and S3. It then discusses serverless benefits like automatic scaling, pay per use, and easier management. The rest of the document demonstrates how to develop, deploy, customize and connect serverless applications, including using AWS services for IDEs, CI/CD pipelines, logging, and connecting to enterprise systems and databases. It also covers GraphQL and AWS AppSync for building real-time data APIs on serverless.
SRV326 Build a Voice-based Chatbot for Your Amazon Connect Contact CenterAmazon Web Services
In this workshop, you learn how easy it is to incorporate a voice-based Amazon Lex chatbot into your Amazon Connect contact center. We walk you through configuring your own Amazon Connect contact center, implementing a chatbot, and using it in your workflows to deliver a personalized voice-based caller experience. You also learn to further personalize caller experiences by using AWS Lambda to access caller information from your customer data system. Leave the session with a functioning Amazon Connect contact center and a voice-enabled chatbot that you can continually modify to your business needs over time. Come prepared to build by bringing your laptop and a phone to make test calls.
by Nader Dabit, Developer Advocate AWS
You’ve got an awesome startup idea – Wild Rydes! The next generation in transportation will be driven by a willing unicorn population and your new startup will produce the worlds first unicorn hailing services. It’s just seven days to launch, and your designers have delivered the final designs for your website, but your idea depends on the mobile economy! Can you build out your web and mobile infrastructure in time for your launch?
Across three days, AWS experts will guide you through all the pieces that are needed to produce an awesome mobile experience for both your unicorns and your riders.
by John Pignata, Startup Solutions Architect, AWS
In this workshop you’ll get a crash course in building and deploying your first serverless application running on AWS Lambda and Amazon API Gateway. You’ll learn to use development tools such as AWS Cloud9, AWS CodeStar, and AWS Serverless Application Model (SAM) to author, deploy, and debug your application. Requirements: laptop, text editor, AWS account, and AWS Command Line Interface (CLI) installed and configured.
by Karthik Saligrama, SDE AWS AppSync
You’ve got an awesome startup idea – Wild Rydes! The next generation in transportation will be driven by a willing unicorn population and your new startup will produce the worlds first unicorn hailing services. It’s just seven days to launch, and your designers have delivered the final designs for your website, but your idea depends on the mobile economy! Can you build out your web and mobile infrastructure in time for your launch?
Across three days, AWS experts will guide you through all the pieces that are needed to produce an awesome mobile experience for both your unicorns and your riders.
Developers need to quickly develop, build, and deploy web applications. In this session, we show you how AWS CodeStar makes it easy for you to set up a continuous delivery toolchain and start developing on AWS in minutes. We also share best practices for managing and deploying web applications using AWS Elastic Beanstalk.
Len Henry
Sr. Solutions Architect, AWS
by Brice Pelle, Enterprise Support Lead, AWS
The designers have included a custom UI for a sign-in and sign-up page, but they forgot to actually include a service sign-up. In this session, we’ll wire up the sign-up and sign-in process with Amazon Cognito and link it to Amazon Pinpoint so you can run campaigns in the future to engage your users.
This document discusses Amazon Web Services' container services and options for running containers on AWS. It provides an overview of containers and why they are popular, such as being portable, lightweight, and easy to deploy. It then describes Amazon's main container services: Amazon Elastic Container Service (ECS) for managing Docker containers, Amazon Elastic Container Service for Kubernetes (EKS) for managing Kubernetes clusters, and AWS Fargate for launching containers without needing to manage infrastructure. It explains that these options provide customers flexibility in how they can run containers on AWS based on their specific needs and workflows.
The document discusses Amazon Web Services (AWS) and provides an overview of its services and capabilities for developers. It notes that AWS allows for quick provisioning of resources without having to rebuild infrastructure, and that it offers a broad and fully-featured platform. The document also lists upcoming presentations at Collision about using AWS for containers, serverless applications, machine learning/AI, and mobile development.
This document discusses infrastructure as code using the AWS Cloud Development Kit (CDK). It begins by describing manual infrastructure creation and then imperative and declarative infrastructure as code approaches. It introduces the CDK, which allows defining infrastructure in familiar programming languages like JavaScript. With the CDK, constructs can be used to provision many underlying AWS resources with a single class, making infrastructure definition more abstract and code-like.
Heeki Park, a senior consultant at AWS, gives a presentation on serverless computing using AWS Lambda. The presentation introduces serverless concepts, demonstrates common use cases for AWS Lambda including web applications, data processing, and IT automation. It also demonstrates how to use AWS Lambda, and how it integrates with other AWS services like API Gateway, Step Functions, Lex and others.
The document discusses AWS Cloud9, a cloud-based integrated development environment (IDE) tool. It was created by AWS to address issues developers face like relying on local machines, the cumbersome setup of development environments, difficulties collaborating on code, and limitations of existing IDEs for serverless applications. AWS Cloud9 allows developers to code in any browser, quickly create new development environments matched to production, and collaboratively code in real time with direct access to AWS services. It is available in several regions and has no charge for using the IDE itself.
The AWS Amplify CLI and tools help developers build full-stack applications by automatically configuring and connecting AWS cloud services like databases, APIs, authentication, analytics, hosting and storage. The CLI creates and manages these services, while the JavaScript library connects front-end apps to the cloud services. Supported frameworks include React, Angular, Vue and Ionic.
This document provides an overview of serverless development using AWS Lambda. It discusses common use cases for serverless applications including web apps, data processing, chatbots, backends, and IT automation. It also covers topics like pricing, resource allocation, available event sources and services, and development tools. The document contains code samples and screenshots related to building serverless applications on AWS Lambda.
Authentication and Identity with Amazon Cognito & Analytics with Amazon PinpointAmazon Web Services
by Adrian Hall, Sr. Developer Advocate, AWS
One of the key challenges for mobile applications is managing users and their identities in order to support monetization strategies, provide differentiated services, and manage fine grained access and data controls. In this session, you’ll learn how Amazon Cognito provides user sign-up and sign-in as part of your onboarding workflow and advanced capabilities for data access/feature management and security.
Once you’ve acquired a user, then the even more difficult task of retaining and monetizing the user begins. In order to retain the user, they must be engaged with the application. In this session, you’ll learn how to use Amazon Pinpoint to implement user engagement strategies via data and event capture, advanced analytics, integrated multi-channel messaging (mobile push, SMS, email) to drive campaigns (both transactional and targeted) based on demographics and audience segmentation.
Build a Voice-Based Chatbot for Your Amazon Connect Contact CenterAmazon Web Services
Learn how easy it is to incorporate a voice-based Amazon Lex chatbot into your Amazon Connect contact center. In this workshop, we walk you through configuring your own Amazon Connect contact center, implementing a chatbot, and using it in your workflows to deliver a personalized voice-based caller experience. You also have the opportunity to further personalize caller experiences by using AWS Lambda to access caller information from your customer data system. Leave this workshop with a functioning Amazon Connect contact center and a voice-enabled chatbot that you can continually modify to your business needs over time. Come prepared to build by bringing your laptop and a phone to make test calls.
by Nader Dabit, Developer Advocate AWS
You’ve got an awesome startup idea – Wild Rydes! The next generation in transportation will be driven by a willing unicorn population and your new startup will produce the worlds first unicorn hailing services. It’s just seven days to launch, and your designers have delivered the final designs for your website, but your idea depends on the mobile economy! Can you build out your web and mobile infrastructure in time for your launch?
Across three days, AWS experts will guide you through all the pieces that are needed to produce an awesome mobile experience for both your unicorns and your riders.
WhereML a Serverless ML Powered Location Guessing Twitter BotRandall Hunt
Learn how we designed, built, and deployed the @WhereML Twitter bot that can identify where in the world a picture was taken using only the pixels in the image. We'll dive deep on artificial intelligence and deep learning with the MXNet framework and also talk about working with the Twitter Account Activity API. The bot is entirely autoscaling and powered by Amazon API Gateway and AWS Lambda which means, as a customer, you don't manage any infrastructure. Finally we'll close with a discussion around custom authorizers in API Gateway and when to use them.
NEW LAUNCH! Integrating Amazon SageMaker into your Enterprise - MCL345 - re:I...Amazon Web Services
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walkthrough the machine learning lifecycle in an integrated AWS environment and discuss best practices.Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
This document discusses Amazon SageMaker, a fully managed machine learning service. It is summarized as follows:
1. Amazon SageMaker provides four main components - notebook instances for data exploration, pre-trained algorithms, a managed training service, and a hosting service to deploy models into production.
2. The training service handles distributed training, saving artifacts and inference images. It supports CPU/GPU and hyperparameter optimization.
3. The hosting service makes it easy to deploy models by creating variants, configurations, and endpoints to serve predictions from trained models with auto-scaling and low latency.
4. Amazon SageMaker aims to simplify and automate all stages of machine learning from data exploration to model deployment.
Supercharge your Machine Learning Solutions with Amazon SageMakerAmazon Web Services
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Working with Amazon SageMaker Algorithms for Faster Model TrainingAmazon Web Services
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides high-performance, machine learning algorithms optimized for speed, scale, and accuracy, to perform training on petabyte-scale data sets. This webinar will introduce you to the collection of distributed streaming ML algorithms that come with Amazon SageMaker. You will learn about the difference between streaming and batch ML algorithms, and how SageMaker has been architected to run these algorithms at scale. We will demo Neural Topic Modeling of text documents using a sample SageMaker Notebook, which will be made available to attendees.
End to End Model Development to Deployment using SageMakerAmazon Web Services
End to End Model Development to Deployment Using SageMaker
In this session we would be developing a model for image classification model (a convolutional neural network, or CNN). We would start off with some theory about CNNs, explore how they learn an image and then proceed towards hands-on lab. We would be using Amazon SageMaker to develop the model in Python, train the model and then to finally create an endpoint and run inference against it. We would be using a custom Conda Kernel for this exercise and would be looking at leveraging SageMaker features like LifeCycle Configurations to help us prepare the notebook before launch. Finally we would be deploying the model in production and run inference against it. We would also be able to monitor various parameters for endpoint performance such as endpoint’s CPU/Memory and Model inference performance metrics.
Level: 200-300
AWS Machine Learning Week SF: End to End Model Development Using SageMakerAmazon Web Services
This document describes Amazon SageMaker's capabilities for end-to-end machine learning model development and deployment. It discusses how SageMaker provides pre-built algorithms and frameworks, managed training and hosting services, and the ability to customize models with user-provided algorithms or frameworks like fast.ai. The document provides an example workflow of using SageMaker to build, train, and deploy a fast.ai model for inference.
Work with Machine Learning in Amazon SageMaker - BDA203 - Toronto AWS SummitAmazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took considerable time and effort, and it required expertise. In this session, we dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker to get your ML models from concept to production.
This document provides an overview of Amazon SageMaker, a fully-managed machine learning platform. It describes the machine learning workflow from problem framing to model deployment and monitoring. SageMaker allows users to build, train, and deploy machine learning models using pre-built algorithms, frameworks like TensorFlow and MXNet, or custom containers. Models can be trained and hosted at scale using SageMaker's notebooks, training jobs, and inference endpoints. Examples and resources for using SageMaker are also provided.
Machine Learning: From Notebook to Production with Amazon Sagemaker (April 2018)Julien SIMON
The document provides an overview of Amazon SageMaker, a fully managed machine learning platform. It describes how SageMaker allows users to build, train, and deploy machine learning models at scale. Key features include pre-built machine learning algorithms, one-click training for ML/DL models, hyperparameter optimization, and deployment of models without engineering effort. The full platform handles tasks like setting up notebook environments, training clusters, writing data connectors, and scaling algorithms to large datasets.
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walkthrough the machine learning lifecycle in an integrated AWS environment and discuss best practices. Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
by Yash Pant, Enterprise Solutions Architect AWS
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walk through the machine learning lifecycle in an integrated AWS environment and discuss best practices. Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
by Roy Ben-Alta, Business Development Manager, AWS
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this session, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walkthrough the machine learning lifecycle in an integrated AWS environment and discuss best practices. Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
Machine Learning: From Notebook to Production with Amazon SagemakerAmazon Web Services
The document discusses Amazon SageMaker, a machine learning platform that allows users to build, train, and deploy machine learning models at scale. It provides pre-built machine learning algorithms and tools for training models using notebooks or custom algorithms. Models can be deployed without additional engineering effort and scaled to production using fully-managed hosting. The platform aims to simplify and automate each step of the machine learning process from data preparation to deployment.
Perform Machine Learning at the IoT Edge using AWS Greengrass and Amazon Sage...Amazon Web Services
"Learning Objectives:
- Develop intelligent IoT edge solutions using AWS Greengrass
- Develop data science models in the cloud with Amazon SageMaker
- Learn how AWS Greengrass and Amazon SageMaker enable you to perform machine learning at the edge"
Integrating Amazon SageMaker into your Enterprise - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Get an introduction to Amazon SageMaker
- Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment
- View a walkthrough of the machine learning lifecycle to cover best practices in the ML process
Predicting the Future with Amazon SageMaker - AWS Summit Sydney 2018Amazon Web Services
The document discusses Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models at scale. It provides an overview of how SageMaker simplifies and automates many complex ML workflow tasks like setting up environments, training models, and deploying models into production. Key features highlighted include built-in algorithms, frameworks and SDK support, hyperparameter tuning, and one-click deployment. Examples are given of using the SageMaker APIs from the command line and Python.
Machine Learning - From Notebook to Production with Amazon SagemakerAmazon Web Services
Learn more about how to deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference.
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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.