Learn how our customers are using the breadth and depth of the artificial intelligence and machine learning offerings on AWS to create new business models and optimise existing processes. Whether you're new to machine learning, or wanting to take your capabilities to the next level, you'll learn how AWS helps you to overcome machine learning challenges on a production scale.
Amazon SageMaker: ML for Every Developer and Data Scientist - AIM202 - Anahei...Amazon Web Services
Machine learning (ML) provides innovation for every business. Until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker—a fully managed service that enables developers to build, train, and deploy ML models at scale—overcomes these barriers. We review its capabilities across data labeling, model building, model training, tuning, and production hosting. We also discuss the details of the modules within Amazon SageMaker, assisting developers through the steps of the ML workflow.
This document provides an overview of artificial intelligence and machine learning techniques. It discusses early concepts of AI from the 1950s, as well as modern machine learning approaches like deep learning using neural networks. Various AI applications are presented, such as autonomous vehicles, facial recognition, and natural language processing. It also introduces Amazon Web Services tools for building AI solutions, including Amazon SageMaker, deep learning frameworks, and GPU-powered EC2 instances. Finally, it promotes the AWS Academy program for teaching AI and cloud computing concepts.
The document discusses Amazon SageMaker, a fully managed machine learning platform. It introduces several new Amazon SageMaker capabilities: Amazon SageMaker Studio, which provides an integrated development environment for machine learning; Amazon SageMaker Notebooks for easier collaboration; Amazon SageMaker Processing for automated data processing and model evaluation; Amazon SageMaker Experiments for organizing and comparing training experiments; Amazon SageMaker Debugger for automated debugging of machine learning models; Amazon SageMaker Model Monitor for continuous monitoring of models in production; and Amazon SageMaker Autopilot for automated machine learning without writing code. It also discusses how Amazon SageMaker addresses challenges in deploying and managing machine learning models at scale.
Learn how to quickly build, train, and deploy machine learning models using Amazon SageMaker, an end-to-end machine learning platform. Amazon SageMaker simplifies machine learning with pre-built algorithms, support for popular deep learning frameworks, such as PyTorch, TensorFlow, and Apache MXNet, as well as one-click model training and deployment.
Running Amazon Elastic Compute Cloud (Amazon EC2) workloads at scale - CMP202...Amazon Web Services
Amazon EC2 Fleet makes it easy to optimize compute performance and cost by blending Amazon EC2 Spot, On-Demand, and Reserved Instances purchasing models. In this session, we learn how to use the power of Amazon EC2 Fleet with AWS services such as AWS Auto Scaling, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Container Service for Kubernetes (Amazon EKS), Amazon EMR, AWS Batch, AWS Thinkbox Deadline, and AWS OpsWorks to programmatically optimize costs while maintaining high performance and availability. We also discuss cost-optimization patterns for workloads such as containers, web services, CI/CD, and big data.
Train once, deploy anywhere on the cloud and at the edge with Amazon SageMake...Amazon Web Services
Developers spend much time and effort delivering machine learning models that can make fast and accurate predictions in real time. These models become even more critical for edge devices where memory and processing power are constrained. Amazon SageMaker Neo lets developers run and develop models in the most optimized way: train the models once and run them anywhere in the cloud and at the edge. In this chalk talk, we dive deep into Neo and show you how this capability of Amazon SageMaker automatically optimizes models built on TensorFlow, Apache MXNet, PyTorch, and ONNX.
Amazon EC2 A1 instances, powered by the AWS Graviton processor - CMP303 - San...Amazon Web Services
Amazon EC2 A1 instances are the first EC2 instances powered by Arm-based AWS Graviton processors. They deliver significant cost savings for scale-out and Arm-based applications, such as web servers, containerized microservices, caching fleets, and distributed
What’s new with Amazon Redshift, featuring ZS Associates - ADB205 - Chicago A...Amazon Web Services
No organization can afford a data warehouse that scales slowly or forces tradeoffs between performance and concurrency. Amazon Redshift scales to provide consistently fast performance with rapidly growing data as well as high user and query concurrency for more than 10,000 customers, including ZS Associates, a professional-services firm serving primarily the Pharmaceutical and Healthcare industries. In this session, we learn how they migrated data-warehousing workloads to Amazon Redshift for scale, agility, cost savings, and performance gain. In addition, they describe their pilot-based approach to migration and the key outcomes achieved. Finally, we highlight recently released and soon-to-come features in Amazon Redshift.
Amazon SageMaker: ML for Every Developer and Data Scientist - AIM202 - Anahei...Amazon Web Services
Machine learning (ML) provides innovation for every business. Until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker—a fully managed service that enables developers to build, train, and deploy ML models at scale—overcomes these barriers. We review its capabilities across data labeling, model building, model training, tuning, and production hosting. We also discuss the details of the modules within Amazon SageMaker, assisting developers through the steps of the ML workflow.
This document provides an overview of artificial intelligence and machine learning techniques. It discusses early concepts of AI from the 1950s, as well as modern machine learning approaches like deep learning using neural networks. Various AI applications are presented, such as autonomous vehicles, facial recognition, and natural language processing. It also introduces Amazon Web Services tools for building AI solutions, including Amazon SageMaker, deep learning frameworks, and GPU-powered EC2 instances. Finally, it promotes the AWS Academy program for teaching AI and cloud computing concepts.
The document discusses Amazon SageMaker, a fully managed machine learning platform. It introduces several new Amazon SageMaker capabilities: Amazon SageMaker Studio, which provides an integrated development environment for machine learning; Amazon SageMaker Notebooks for easier collaboration; Amazon SageMaker Processing for automated data processing and model evaluation; Amazon SageMaker Experiments for organizing and comparing training experiments; Amazon SageMaker Debugger for automated debugging of machine learning models; Amazon SageMaker Model Monitor for continuous monitoring of models in production; and Amazon SageMaker Autopilot for automated machine learning without writing code. It also discusses how Amazon SageMaker addresses challenges in deploying and managing machine learning models at scale.
Learn how to quickly build, train, and deploy machine learning models using Amazon SageMaker, an end-to-end machine learning platform. Amazon SageMaker simplifies machine learning with pre-built algorithms, support for popular deep learning frameworks, such as PyTorch, TensorFlow, and Apache MXNet, as well as one-click model training and deployment.
Running Amazon Elastic Compute Cloud (Amazon EC2) workloads at scale - CMP202...Amazon Web Services
Amazon EC2 Fleet makes it easy to optimize compute performance and cost by blending Amazon EC2 Spot, On-Demand, and Reserved Instances purchasing models. In this session, we learn how to use the power of Amazon EC2 Fleet with AWS services such as AWS Auto Scaling, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Container Service for Kubernetes (Amazon EKS), Amazon EMR, AWS Batch, AWS Thinkbox Deadline, and AWS OpsWorks to programmatically optimize costs while maintaining high performance and availability. We also discuss cost-optimization patterns for workloads such as containers, web services, CI/CD, and big data.
Train once, deploy anywhere on the cloud and at the edge with Amazon SageMake...Amazon Web Services
Developers spend much time and effort delivering machine learning models that can make fast and accurate predictions in real time. These models become even more critical for edge devices where memory and processing power are constrained. Amazon SageMaker Neo lets developers run and develop models in the most optimized way: train the models once and run them anywhere in the cloud and at the edge. In this chalk talk, we dive deep into Neo and show you how this capability of Amazon SageMaker automatically optimizes models built on TensorFlow, Apache MXNet, PyTorch, and ONNX.
Amazon EC2 A1 instances, powered by the AWS Graviton processor - CMP303 - San...Amazon Web Services
Amazon EC2 A1 instances are the first EC2 instances powered by Arm-based AWS Graviton processors. They deliver significant cost savings for scale-out and Arm-based applications, such as web servers, containerized microservices, caching fleets, and distributed
What’s new with Amazon Redshift, featuring ZS Associates - ADB205 - Chicago A...Amazon Web Services
No organization can afford a data warehouse that scales slowly or forces tradeoffs between performance and concurrency. Amazon Redshift scales to provide consistently fast performance with rapidly growing data as well as high user and query concurrency for more than 10,000 customers, including ZS Associates, a professional-services firm serving primarily the Pharmaceutical and Healthcare industries. In this session, we learn how they migrated data-warehousing workloads to Amazon Redshift for scale, agility, cost savings, and performance gain. In addition, they describe their pilot-based approach to migration and the key outcomes achieved. Finally, we highlight recently released and soon-to-come features in Amazon Redshift.
[REPEAT] Optimize your workloads with Amazon EC2 & AMD EPYC - DEM01-R - Santa...Amazon Web Services
Customers are always looking to optimize the performance of their workloads while lowering their cost. With new Amazon EC2 instances powered by AMD EPYC processors, customer can do just that. Join AMD and AWS as they jointly showcase how AMD-powered M5a, R5a, and T3a EC2 instances can save you 10% on infrastructure costs for right-sized workloads. Discover the benefits, use cases, and customer successes of these new instances.
This document discusses machine learning using Kubernetes. It provides an overview of Amazon EKS for running Kubernetes in the cloud, and options for setting up Kubernetes clusters for machine learning workloads, including training models, inference, and applications. It also covers challenges in containerizing machine learning and introduces AWS deep learning containers and KubeFlow for simplifying machine learning on Kubernetes.
IoT transformation begins at home, but how can you get started quickly? Voice is a natural interface to interact not just with the world around us but also with physical assets and things, such as connected home devices like lights, thermostats, or TVs. In this session, we discuss how you can connect and control devices in your home using AWS IoT services and the Alexa Skills Kit. By the end of the session, you’ll have a set of best practices for how to build IoT products in the connected home.
Add intelligence to applications with AWS AI services - AIM201 - New York AWS...Amazon Web Services
AI has already been integrated into many use cases, but we’ve only scratched the surface of what’s possible. In this session, we cover how to use the AWS AI services to tackle three use cases that can deliver immediate value: “voice of the customer” analytics to better understand what your customers are thinking and saying, document analysis and processing to move beyond the limitations of traditional OCR, and chatbots to improve in-app customer service and customer contact center experiences. We also discuss how to use AI within the Media, Healthcare, and Financial Services industries.
Grid computing in the cloud for Financial Services industry - CMP205-I - New ...Amazon Web Services
This document discusses using cloud computing on AWS for grid computing in the financial services industry. It notes that financial modeling has become more complex, requiring more data and scenarios. On-premises grids often cannot meet these demands due to limited capacity. The cloud provides elastic, on-demand compute resources without large upfront hardware investments. AWS services like EC2, FSx, and Batch allow building scalable HPC clusters that can quickly scale up and down based on demand. Partners like Accenture help financial firms use AWS to perform risk calculations and meet regulatory requirements more cost effectively.
Migliora la disponibilità e le prestazioni delle tue applicazioni con Amazon ...Amazon Web Services
AWS Summit Milano 2019 - Migliora la disponibilità e le prestazioni delle tue applicazioni con Amazon Global Network - Marco Cagna, Sr. Product Manager, AWS | Cliente: Pegaso Università
How Nubank is building a customer-obsessed bank - FSV201 - New York AWS SummitAmazon Web Services
Nubank, Latin America’s first (and largest) cloud-native bank, has relied on AWS since day one. Operating in the cloud allows Nubank’s developers to create software that scales and quickly adapts to the changing needs of a complex market and a growing business. Nubank relies on services like Amazon EC2, Amazon DynamoDB, Amazon VPC, Amazon S3, and AWS CloudFormation to let 8.5 million customers make around 2.5 million purchases per day—all without a dedicated infrastructure team. Learn how Nubank’s fully automated, cell-based architecture allows the bank to provide the best customer experience while generating reliable audited financial records for regulators.
Maschinelles Lernen auf AWS für Entwickler, Data Scientists und ExpertenAWS Germany
In diesem Vortrag geben wir einen Überblick mit Beispielen über aktuelle Werkzeuge für Maschinelles Lernen (ML) auf AWS. Dieser überblick deckt alle Möglichkeiten von einfach zu nutzenden, vollständig verwalteten ML-Services für Entwickler über ML-Plattformen für Data Scientists bis hin zu ML-optimierten Infrastruktur- und Software-Komponenten ab. Beispiele und Online-Demos zeigen, wie einfach ML-Methoden auf AWS genutzt werden können.
Moderator: Christian Petters, Solutions Architect, AWS
Analyze customer sentiment using AI - AIM307 - New York AWS SummitAmazon Web Services
Gaining insights into your customers provides a low-cost way to acquire leads, improve website traffic, develop customer relationships, and improve customer service, enabling you to improve the products or services that you provide. In this chalk talk, we walk through how to leverage AWS AI services to build an intelligent serverless pipeline and a social media dashboard, giving you customer insights. We demonstrate how to ingest data from both Twitter and Reddit, and we show you how to add social media sources, translate between languages, analyze sentiment, entities, and key phrases, and how to build a dashboard using this information. Join us as we step through the architecture and provide code examples at each layer.
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Amazon Web Services
Machine learning (ML) offers innovation for every business. But until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale, overcomes these barriers. We review its capabilities, including data labeling, model building, model training, tuning, and production hosting.
This talk will feature a list of quick-hitting pro tips aimed at improving your day-to-day life as a developer building on AWS. This session will cover tips on: working effectively with the AWS CLI and other third-party CLIs; creating, editing, debugging, and deploying an AWS Lambda-powered serverless application quickly and easily using the new AWS Toolkit; and performing powerful filtering and searches on your structured application logs with Amazon CloudWatch.
Introduction to EC2 A1 instances, powered by the AWS Graviton processor - CMP...Amazon Web Services
Amazon EC2 A1 instances are the first EC2 instances powered by Arm-based AWS Graviton processors. They deliver significant cost savings for scale-out and Arm-based applications, such as web servers, containerized microservices, caching fleets, and distributed data stores that are supported by the extensive Arm ecosystem. In this chalk talk, learn about EC2 A1 instances, understand the use cases, and watch demonstrations of how easy it can be to migrate and run your workloads on EC2 A1. Discussion and questions are encouraged.
Build intelligent applications quickly with AWS AI services - AIM301 - New Yo...Amazon Web Services
How do you build AI applications without machine learning skills? In this workshop, you get hands-on experience using AI tools for text-to-speech, translation, natural language processing, and personalization. You also learn some practical ways to integrate these AI capabilities into common use cases, such as contact center speech analytics, social media analytics, and personalized recommendations. To participate in this workshop, you must have an AWS account. We provide you with credits.
Enterprises dream of hybrid applications across on-premise and public clouds. Today, we have enterprise applications running largely on-premise while most web-scale applications are built in the cloud. The cloud divide is real and inhibits enterprises from getting leverage in a hybrid cloud world. There is a need for enterprises to unify their multiple clouds and on-premise.
Build accurate training data sets with Amazon SageMaker Ground Truth - AIM302...Amazon Web Services
Successful machine learning (ML) models are built on high-quality training datasets. However, it is often expensive, complicated, and time-consuming to create the training data necessary to build these models. Amazon SageMaker Ground Truth helps you quickly build highly accurate training datasets for ML. SageMaker Ground Truth offers easy access to public and private human labelers, providing them with built-in workflows and interfaces for common labeling tasks. Additionally, SageMaker Ground Truth uses automatic labeling, lowering your labeling costs by up to 70%. In this chalk talk, we dive deep into using SageMaker Ground Truth to build high-quality training datasets.
Add intelligence to applications - AIM205 - Santa Clara AWS Summit.pdfAmazon Web Services
1. The document discusses Amazon Web Services machine learning and artificial intelligence services including Amazon Textract, Amazon Transcribe, Amazon Translate, and Amazon Comprehend.
2. It provides examples of how these services can be used to extract text, tables, and forms from documents, transcribe speech, translate languages, and derive insights from text through natural language processing.
3. The document also outlines reference architectures for indexing and searching documents, capturing forms, and extracting insights from text using these AI services.
Migrating on-premises Apache Spark and Hive to Amazon EMR - ADB304 - New York...Amazon Web Services
Want to gain agility, reduce costs, and reduce risk by migrating your on-premises Apache Spark and Hive workloads to Amazon EMR? Learn how to re-architect vs. just doing a “lift and shift.” Discover how to separate compute and storage, build a catalog, check data quality, estimate and optimize costs, and secure everything. Get advice for Apache HBase, Presto, and Impala applications too!
AWS Summit Milano 2019 - Creare e gestire Data Lake e Data Warehouses - Giorgio Nobile, Solutions Architect, AWS | Francesco Marelli, Solutions Architect, AWS | Cliente: THRON
Optimizing Cost and Capacity for Compute - CMP302 - Santa Clara AWS SummitAmazon Web Services
Amazon EC2 offers a wide variety of compute instances via highly flexible pricing options that are well suited for every use case, including web services, containerized workloads, batch processing, big data, CI/CD, and supercomputing. In this session, learn how you can optimize your workloads running on EC2 for cost and performance, all while handling peak demand. See demos on how to deploy highly-available cost-optimized workloads at scale using EC2 Launch Templates, EC2 Fleet, and EC2 Auto Scaling
This session covers best practices, features, and capabilities that users of Microsoft products can leverage in AWS. We emphasize Windows Server, Microsoft SQL Server, Active Directory, and .NET capabilities available and deeply integrated in AWS. With these learnings, you can extend the value of your Microsoft investments, lower total cost of ownership (TCO), and keep users working in familiar environments.
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Amazon Web Services
Machine learning (ML) offers innovation for every business. But until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale, overcomes those challenges. We review its capabilities, including data labeling, model building, model training, tuning, and production hosting.
ML for every developer and data scientist with Amazon SageMaker - AIM201 - At...Amazon Web Services
Machine learning (ML) provides innovation for every business. Until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker makes developing ML models faster and easier. Amazon SageMaker is a fully managed service that enables developers to build, train, and deploy ML models at scale. We review its capabilities across data labeling, model building, model training, tuning, and production hosting.
[REPEAT] Optimize your workloads with Amazon EC2 & AMD EPYC - DEM01-R - Santa...Amazon Web Services
Customers are always looking to optimize the performance of their workloads while lowering their cost. With new Amazon EC2 instances powered by AMD EPYC processors, customer can do just that. Join AMD and AWS as they jointly showcase how AMD-powered M5a, R5a, and T3a EC2 instances can save you 10% on infrastructure costs for right-sized workloads. Discover the benefits, use cases, and customer successes of these new instances.
This document discusses machine learning using Kubernetes. It provides an overview of Amazon EKS for running Kubernetes in the cloud, and options for setting up Kubernetes clusters for machine learning workloads, including training models, inference, and applications. It also covers challenges in containerizing machine learning and introduces AWS deep learning containers and KubeFlow for simplifying machine learning on Kubernetes.
IoT transformation begins at home, but how can you get started quickly? Voice is a natural interface to interact not just with the world around us but also with physical assets and things, such as connected home devices like lights, thermostats, or TVs. In this session, we discuss how you can connect and control devices in your home using AWS IoT services and the Alexa Skills Kit. By the end of the session, you’ll have a set of best practices for how to build IoT products in the connected home.
Add intelligence to applications with AWS AI services - AIM201 - New York AWS...Amazon Web Services
AI has already been integrated into many use cases, but we’ve only scratched the surface of what’s possible. In this session, we cover how to use the AWS AI services to tackle three use cases that can deliver immediate value: “voice of the customer” analytics to better understand what your customers are thinking and saying, document analysis and processing to move beyond the limitations of traditional OCR, and chatbots to improve in-app customer service and customer contact center experiences. We also discuss how to use AI within the Media, Healthcare, and Financial Services industries.
Grid computing in the cloud for Financial Services industry - CMP205-I - New ...Amazon Web Services
This document discusses using cloud computing on AWS for grid computing in the financial services industry. It notes that financial modeling has become more complex, requiring more data and scenarios. On-premises grids often cannot meet these demands due to limited capacity. The cloud provides elastic, on-demand compute resources without large upfront hardware investments. AWS services like EC2, FSx, and Batch allow building scalable HPC clusters that can quickly scale up and down based on demand. Partners like Accenture help financial firms use AWS to perform risk calculations and meet regulatory requirements more cost effectively.
Migliora la disponibilità e le prestazioni delle tue applicazioni con Amazon ...Amazon Web Services
AWS Summit Milano 2019 - Migliora la disponibilità e le prestazioni delle tue applicazioni con Amazon Global Network - Marco Cagna, Sr. Product Manager, AWS | Cliente: Pegaso Università
How Nubank is building a customer-obsessed bank - FSV201 - New York AWS SummitAmazon Web Services
Nubank, Latin America’s first (and largest) cloud-native bank, has relied on AWS since day one. Operating in the cloud allows Nubank’s developers to create software that scales and quickly adapts to the changing needs of a complex market and a growing business. Nubank relies on services like Amazon EC2, Amazon DynamoDB, Amazon VPC, Amazon S3, and AWS CloudFormation to let 8.5 million customers make around 2.5 million purchases per day—all without a dedicated infrastructure team. Learn how Nubank’s fully automated, cell-based architecture allows the bank to provide the best customer experience while generating reliable audited financial records for regulators.
Maschinelles Lernen auf AWS für Entwickler, Data Scientists und ExpertenAWS Germany
In diesem Vortrag geben wir einen Überblick mit Beispielen über aktuelle Werkzeuge für Maschinelles Lernen (ML) auf AWS. Dieser überblick deckt alle Möglichkeiten von einfach zu nutzenden, vollständig verwalteten ML-Services für Entwickler über ML-Plattformen für Data Scientists bis hin zu ML-optimierten Infrastruktur- und Software-Komponenten ab. Beispiele und Online-Demos zeigen, wie einfach ML-Methoden auf AWS genutzt werden können.
Moderator: Christian Petters, Solutions Architect, AWS
Analyze customer sentiment using AI - AIM307 - New York AWS SummitAmazon Web Services
Gaining insights into your customers provides a low-cost way to acquire leads, improve website traffic, develop customer relationships, and improve customer service, enabling you to improve the products or services that you provide. In this chalk talk, we walk through how to leverage AWS AI services to build an intelligent serverless pipeline and a social media dashboard, giving you customer insights. We demonstrate how to ingest data from both Twitter and Reddit, and we show you how to add social media sources, translate between languages, analyze sentiment, entities, and key phrases, and how to build a dashboard using this information. Join us as we step through the architecture and provide code examples at each layer.
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Amazon Web Services
Machine learning (ML) offers innovation for every business. But until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale, overcomes these barriers. We review its capabilities, including data labeling, model building, model training, tuning, and production hosting.
This talk will feature a list of quick-hitting pro tips aimed at improving your day-to-day life as a developer building on AWS. This session will cover tips on: working effectively with the AWS CLI and other third-party CLIs; creating, editing, debugging, and deploying an AWS Lambda-powered serverless application quickly and easily using the new AWS Toolkit; and performing powerful filtering and searches on your structured application logs with Amazon CloudWatch.
Introduction to EC2 A1 instances, powered by the AWS Graviton processor - CMP...Amazon Web Services
Amazon EC2 A1 instances are the first EC2 instances powered by Arm-based AWS Graviton processors. They deliver significant cost savings for scale-out and Arm-based applications, such as web servers, containerized microservices, caching fleets, and distributed data stores that are supported by the extensive Arm ecosystem. In this chalk talk, learn about EC2 A1 instances, understand the use cases, and watch demonstrations of how easy it can be to migrate and run your workloads on EC2 A1. Discussion and questions are encouraged.
Build intelligent applications quickly with AWS AI services - AIM301 - New Yo...Amazon Web Services
How do you build AI applications without machine learning skills? In this workshop, you get hands-on experience using AI tools for text-to-speech, translation, natural language processing, and personalization. You also learn some practical ways to integrate these AI capabilities into common use cases, such as contact center speech analytics, social media analytics, and personalized recommendations. To participate in this workshop, you must have an AWS account. We provide you with credits.
Enterprises dream of hybrid applications across on-premise and public clouds. Today, we have enterprise applications running largely on-premise while most web-scale applications are built in the cloud. The cloud divide is real and inhibits enterprises from getting leverage in a hybrid cloud world. There is a need for enterprises to unify their multiple clouds and on-premise.
Build accurate training data sets with Amazon SageMaker Ground Truth - AIM302...Amazon Web Services
Successful machine learning (ML) models are built on high-quality training datasets. However, it is often expensive, complicated, and time-consuming to create the training data necessary to build these models. Amazon SageMaker Ground Truth helps you quickly build highly accurate training datasets for ML. SageMaker Ground Truth offers easy access to public and private human labelers, providing them with built-in workflows and interfaces for common labeling tasks. Additionally, SageMaker Ground Truth uses automatic labeling, lowering your labeling costs by up to 70%. In this chalk talk, we dive deep into using SageMaker Ground Truth to build high-quality training datasets.
Add intelligence to applications - AIM205 - Santa Clara AWS Summit.pdfAmazon Web Services
1. The document discusses Amazon Web Services machine learning and artificial intelligence services including Amazon Textract, Amazon Transcribe, Amazon Translate, and Amazon Comprehend.
2. It provides examples of how these services can be used to extract text, tables, and forms from documents, transcribe speech, translate languages, and derive insights from text through natural language processing.
3. The document also outlines reference architectures for indexing and searching documents, capturing forms, and extracting insights from text using these AI services.
Migrating on-premises Apache Spark and Hive to Amazon EMR - ADB304 - New York...Amazon Web Services
Want to gain agility, reduce costs, and reduce risk by migrating your on-premises Apache Spark and Hive workloads to Amazon EMR? Learn how to re-architect vs. just doing a “lift and shift.” Discover how to separate compute and storage, build a catalog, check data quality, estimate and optimize costs, and secure everything. Get advice for Apache HBase, Presto, and Impala applications too!
AWS Summit Milano 2019 - Creare e gestire Data Lake e Data Warehouses - Giorgio Nobile, Solutions Architect, AWS | Francesco Marelli, Solutions Architect, AWS | Cliente: THRON
Optimizing Cost and Capacity for Compute - CMP302 - Santa Clara AWS SummitAmazon Web Services
Amazon EC2 offers a wide variety of compute instances via highly flexible pricing options that are well suited for every use case, including web services, containerized workloads, batch processing, big data, CI/CD, and supercomputing. In this session, learn how you can optimize your workloads running on EC2 for cost and performance, all while handling peak demand. See demos on how to deploy highly-available cost-optimized workloads at scale using EC2 Launch Templates, EC2 Fleet, and EC2 Auto Scaling
This session covers best practices, features, and capabilities that users of Microsoft products can leverage in AWS. We emphasize Windows Server, Microsoft SQL Server, Active Directory, and .NET capabilities available and deeply integrated in AWS. With these learnings, you can extend the value of your Microsoft investments, lower total cost of ownership (TCO), and keep users working in familiar environments.
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Amazon Web Services
Machine learning (ML) offers innovation for every business. But until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale, overcomes those challenges. We review its capabilities, including data labeling, model building, model training, tuning, and production hosting.
ML for every developer and data scientist with Amazon SageMaker - AIM201 - At...Amazon Web Services
Machine learning (ML) provides innovation for every business. Until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker makes developing ML models faster and easier. Amazon SageMaker is a fully managed service that enables developers to build, train, and deploy ML models at scale. We review its capabilities across data labeling, model building, model training, tuning, and production hosting.
Learn how to get started with Amazon SageMaker, our fully-managed service that spans the entire machine learning (ML) workflow, so you can build, train, and deploy models quickly. Use Amazon SageMaker to label and prepare your data, choose an algorithm, train, tune, and optimise it for deployment, make predictions, and take action.
Get your models to production faster with Amazon SageMaker SDKs, builder tools, and APIs tailored to your programming language or platform. Also, discover how Amazon SageMaker Ground Truth can aid in the adoption of ML technology for your organisation.
Learn how to get started with Amazon SageMaker—our fully-managed service that spans the entire machine learning (ML) workflow—so you can build, train, and deploy models quickly. Use Amazon SageMaker to label and prepare your data, choose an algorithm, train, tune, and optimize it for deployment, make predictions, and take action. Get your models to production faster with Amazon SageMaker SDKs, builder tools, and APIs tailored to your programming language or platform. Also, discover how Amazon SageMaker Ground Truth can aid in the adoption of ML technology for your organization.
AWS has introduced Deep Learning Containers to help users set up optimized deep learning environments for training and inference using TensorFlow and MXNet. The containers provide best performance automatically and support both single-node and multi-node setups on AWS services like ECS, EKS and EC2. Wix uses deep learning for tasks like super resolution, portrait segmentation and layout optimization. They found that using AWS Deep Learning Containers could decrease their time to production for new deep learning models by 20% by providing consistent environments for research, training and deployment.
Build, train and deploy Machine Learning models on Amazon SageMaker (May 2019)Julien SIMON
1) British Airways uses Amazon SageMaker to build machine learning models to predict aircraft maintenance needs from flight data collected from over 290 aircraft.
2) The presentation provides an overview of British Airways' use of Amazon SageMaker, including how they process and analyze flight data, build models to detect issues, and plan to expand their use of SageMaker for additional predictive maintenance.
3) Amazon SageMaker is highlighted as a tool that allows British Airways to easily build, train, deploy and manage machine learning models for predictive maintenance without having to manage any underlying infrastructure.
Build, train and deploy machine learning models at scale using AWSAmazon Web Services
Learn how to quickly build, train, and deploy machine learning models using Amazon SageMaker, an end-to-end machine learning platform. Amazon SageMaker simplifies machine learning with pre-built algorithms, support for popular deep learning frameworks, such as PyTorch, TensorFlow, and Apache MXNet, as well as one-click model training and deployment.
Work with Machine Learning in Amazon SageMaker - BDA203 - Atlanta 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.
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"
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.
This document provides information about an Italian language webinar series on artificial intelligence hosted by Davide Gallo from AWS. The webinars will cover getting started with AI at Amazon, the AWS AI/ML platform, use cases, and customer success stories. The first webinar on June 13th will focus on the AI capabilities at Amazon, the AWS platform for AI/ML, and use cases of successful companies.
Demystifying Machine Learning On AWS - AWS Summit Sydney 2018Amazon Web Services
Demystifying Machine Learning on AWS
Machine Learning is having a major impact in our society, but how can we simplify the build, train, and deploy process for all developers and data scientists? Understand how cloud-based machine learning frameworks can help turn your data into intelligence. We will introduce the general machine learning process utilising the AWS Deep Learning AMIs and hear from carsales.com.au about how they built the Cyclops, a Super Human Image Recognition Software on AWS. We will then discuss the new capabilities delivered by Amazon SageMaker and how this product will further reduce the undifferentiated heavy lifting; freeing you up to focus on your business and allow your developers to quickly and easily expand into the world of Machine Learning.
Jenny Davies, Solutions Architect, Amazon Web Services and Agustinus Nalwan, AI and Machine Learning Technical Development Manager, Carsales.com.au
The document discusses Amazon Web Services (AWS) machine learning and artificial intelligence services that were highlighted at re:Invent 2019. It summarizes new capabilities for Amazon SageMaker such as Ground Truth for data labeling, Reinforcement Learning support, Neo for optimized model deployment, and the AWS Marketplace for Machine Learning. It also discusses Amazon Personalize for recommendations, Forecast for time series forecasting, and Textract for extracting text and data from documents. The document provides an overview of AWS database services including DynamoDB, Timestream, and QLDB. It discusses Amazon Managed Blockchain and new Lambda capabilities like layers and Application Load Balancer targets.
Migrating Real-Time Sports Scores to the Cloud via Low-Latency Messaging (API...Amazon Web Services
In this session, learn how media company Turner Broadcasting delivers real-time sports scores to high-profile sites like the NCAA and PGA using Amazon MQ. Gain a deeper understanding of how Turner migrated from their on-premises message broker to Amazon MQ, and was able to preserve the low-latency messaging expected by their customers. Expect to leave with insights on the migration process, including the surprisingly fast timelines, and the benefits of a managed message broker service.
엔터프라이즈의 인공지능(AI)과 머신러닝(ML) 적용은 왜 어려울까요?
성공적인 AI과 ML 적용.
베스핀글로벌의 웨비나 자료를 통해서 Amazon AI/ML에 대해 알아보세요.
[Agenda]
1. Machine Learning at Amazon
2. Machine Learning on AWS
- Frameworks and Interfaces
- AWS ML Platform services
- AWS ML Application services
High performance and cost optimised need not be an oxymoron in AWS. In this session we will focus on practical ways to build efficient, high performance architectures. We will focus on how to pick the most appropriate service and resources, understand trade-offs, and reduce costs over time.
Art of the possible- Leveraging Machine Learning to Improve Forecasting and G...Amazon Web Services
Challenge: Customers require enhanced spend forecasting and prediction in order to optimize their AWS usage and more accurately track, monitor, and budget their spend. Solution: In support of our AWS MSP and reseller capability and business, ECS developed our own cloud management portal (Common Cloud) which processes thousands of billing records on a daily basis. We’ve deployed AWS ML solutions to support advanced financial analysis of trends/usage for both customers and our AWS business unit and to deliver advanced forecasting and prediction models for monthly costs using a regression-based linear learner model. This session is sponsored by ECS.
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.