SKL208 - Turbocharge your Business with AI and Machine Learning - Tel Aviv Su...Boaz Ziniman
This session will focus on the basic building blocks of Artificial Intelligence (AI) and Machine Learning (ML) using AWS services. It will help you to identify use cases for ML with real-world examples, and help you create the right conditions for delivering successful ML-based solutions to your business.
This document discusses using machine learning for fraud detection and risk management. It provides examples of use cases like credit card fraud detection and outlines the challenges of rules-based approaches. The document then introduces the Amazon ML stack and services like Amazon Comprehend and Amazon SageMaker. It provides an example of how FINRA uses natural language processing to accelerate fraud investigations. Finally, it discusses how Intuit was able to cut their model development time from 6 months to 1 week using Amazon SageMaker.
Adding to the existing AI services, AWS continues to bridge the gap for developers to build ML solutions without the hurdle of having data science expertise. In this session, learn about the new services announced at re:Invent (Forecast, Textract and Personalize) and get a preview of what to expect when building time series models, OCR and recommendation engines with little to no data science experience.
Microservices on AWS: Architectural Patterns and Best Practices | AWS Summit ...AWS Summits
This document summarizes a presentation on architecting microservices on AWS. It discusses using AWS services like API Gateway, ECS, Lambda, SNS and Cloud Map to build scalable and resilient microservices architectures. It also provides an example "AWSome Airlines" architecture showing how different services like a frontend, data microservices, machine learning services and a serverless scheduler can be integrated. Design concepts discussed include leveraging managed services, having loosely coupled and event-driven systems, and simplifying delivery and discovery.
The document discusses CLP Innovation Enterprise Ltd., a company that generates 23,707 MW of energy and has built an app store called Smart Energy Connect to accelerate innovation. It describes how building apps on a serverless infrastructure using AWS has reduced costs by 80% and infrastructure staff requirements to only 20%. The app store contains apps in categories like offices, solar, and visibility that make sustainability practical.
SKL208 - Turbocharge your Business with AI and Machine Learning - Tel Aviv Su...Boaz Ziniman
This session will focus on the basic building blocks of Artificial Intelligence (AI) and Machine Learning (ML) using AWS services. It will help you to identify use cases for ML with real-world examples, and help you create the right conditions for delivering successful ML-based solutions to your business.
This document discusses using machine learning for fraud detection and risk management. It provides examples of use cases like credit card fraud detection and outlines the challenges of rules-based approaches. The document then introduces the Amazon ML stack and services like Amazon Comprehend and Amazon SageMaker. It provides an example of how FINRA uses natural language processing to accelerate fraud investigations. Finally, it discusses how Intuit was able to cut their model development time from 6 months to 1 week using Amazon SageMaker.
Adding to the existing AI services, AWS continues to bridge the gap for developers to build ML solutions without the hurdle of having data science expertise. In this session, learn about the new services announced at re:Invent (Forecast, Textract and Personalize) and get a preview of what to expect when building time series models, OCR and recommendation engines with little to no data science experience.
Microservices on AWS: Architectural Patterns and Best Practices | AWS Summit ...AWS Summits
This document summarizes a presentation on architecting microservices on AWS. It discusses using AWS services like API Gateway, ECS, Lambda, SNS and Cloud Map to build scalable and resilient microservices architectures. It also provides an example "AWSome Airlines" architecture showing how different services like a frontend, data microservices, machine learning services and a serverless scheduler can be integrated. Design concepts discussed include leveraging managed services, having loosely coupled and event-driven systems, and simplifying delivery and discovery.
The document discusses CLP Innovation Enterprise Ltd., a company that generates 23,707 MW of energy and has built an app store called Smart Energy Connect to accelerate innovation. It describes how building apps on a serverless infrastructure using AWS has reduced costs by 80% and infrastructure staff requirements to only 20%. The app store contains apps in categories like offices, solar, and visibility that make sustainability practical.
Adding intelligence to applications - AIM201 - Chicago AWS SummitAmazon Web Services
AI has already been integrated into many use cases, but we've just 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: 1) “voice of the customer” analytics to better understand what your customers are thinking and saying; 2) document analysis and processing to move beyond the limitations of traditional OCR; and 3) chatbots to improve in-app customer service and customer contact center experiences. We also discuss how to use AI in use cases within the media, healthcare, and financial services industries.
This document discusses Amazon SageMaker, an AWS service that helps address common machine learning workflow problems. It automates much of the infrastructure setup required for machine learning and provides pre-built algorithms, notebooks, processing resources and more. The document outlines typical machine learning workflows for development, training and inference, and how SageMaker addresses issues like environment setup, distributed training and model deployment. It also provides an overview of the SageMaker architecture and SDK development process.
Solve complex business problems with managed ML services.pdfAmazon Web Services
Personalization and forecasting have long been very complex problems to solve for organizations. In this session, we'll show you how to use Amazon Personalize and Amazon Forecast, two new services that let you create individualized recommendations for customers, and deliver highly accurate forecasts. Both run on fully-managed infrastructure, and provide easy-to-use recipes that deliver high-quality models even if you have little Machine Learning experience.
Amazon SageMaker Ground Truth: Build High-Quality and Accurate ML Training Da...Amazon Web Services
Successful machine learning models are built on high-quality training datasets. Labeling raw data to get accurate training datasets involves a lot of time and effort because sophisticated models can require thousands of labeled examples to learn from, before they can produce good results. Typically, the task of labeling is distributed across a large number of humans, adding significant overhead and cost. Join us as we introduce Amazon SageMaker Ground Truth, a new service that provides an effective solution to reduce this cost and complexity using a machine learning technique called active learning. Active learning reduces the time and manual effort required to do data labeling, by continuously training machine learning algorithms based on labels from humans. By iterating through ambiguous data points, Ground Truth improves the ability to automatically label data resulting in high-quality training datasets.
Level: 300
Speaker: Kris Skrinak - Partner Solutions Architect, ML Global Lead, AWS
Sviluppa, addestra e distribuisci modelli di machine learning.pdfAmazon Web Services
The document discusses Amazon SageMaker, an AWS managed service for building, training, and deploying machine learning models. It provides an overview of the key capabilities of SageMaker such as using built-in algorithms, bringing your own algorithms/containers, hyperparameter tuning, hosting models for inference, and batch transforms. It also discusses how SageMaker integrates with other AWS services like S3, EC2, and Marketplace.
Previously, ETL meant using proprietary products with commercial databases and users with specialist skills. Learn how to create ETL data pipelines that can securely consume data at scale while using open source technologies and languages to enable your organisation, team, and data.
Speaker: Paul Macey, Big Data Specialist, AWS
Bonus-Session-Interledger-DvP-Settlement-on-Amazon-Managed-BlockchainAmazon Web Services
Project Ubin is a collaborative project first launched in 2016 by the Monetary Authority of Singapore (MAS) and the Association of Banks in Singapore (ABS). In August 2018, MAS and SGX announced partnership with Anquan, Deloitte and Nasdaq to harness blockchain technology for settlement of tokenised assets. In order to explore the possible DvP settlement models, six blockchains of varied capabilities and features were used in prototypes. In this session, Peter from SGX will offer deeper insights into a DvP prototype developed with Deloitte that was hosted on Amazon Managed Blockchain, and will also discuss potential future applications of DLT (Distributed Ledger Technology) for capital markets, while Michael from AWS will discuss some of the common patterns seen in blockchain networks as well as features that control the privacy of data on shared blockchain networks.
AIM301 - Breaking Language Barriers With AI - Tel Aviv Summit 2019Boaz Ziniman
AI and Machine learning allow developers to introduce new language capabilities in their apps and use Natural Language Processing and Natural Language Understanding to break language barriers, add new functionality and expand their target audience. This session will focus on several AWS AI services for developers, that allow you to add such functionality to your code with minimal effort. We will build an automatic translator, interact with text to speech and try to extract sentiments from live text coming from different feeds.
In this presentation, we will explore three different ways you can build backends for real-time applications. First, we'll examine how to run and scale SignalR and other similar opensource frameworks on AWS using Amazon ElastiCache. Next, we will take a look at the new WebSocket support in API gateway and discover how it enables us to convert Lambda functions into real-time solutions. Finally, we will look at AppSyncs incredible real-time support for web and mobile applications
Generational Shifts and customer expectations has greatly changed the way insurance works, affecting insurer's channel, product and brand strategies. New players ike virtual insurers are getting ahead in the game. In this session, Bowtie, the first virtual insurer in Hong Kong will dive deep into how they leverage the AWS cloud technologies to build a new operations model, accelerate their business and minimize capital investment.
Performing real-time ETL into data lakes - ADB202 - Santa Clara AWS Summit.pdfAmazon Web Services
In this session, we discuss several options for performing real-time extract, transform, and load (ETL) using Amazon Kinesis, AWS Lambda, AWS Glue, and Amazon S3. We provide an overview of the different options that have distinct advantages in building real-time ETL applications before loading a data lake or warehouse.
Build, train, and deploy ML models with Amazon SageMaker - AIM302 - New York ...Amazon 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 models at any scale. In this workshop, you learn how to build an ML model using Amazon SageMaker's built-in algorithms and frameworks. We then train the model using automatic model tuning to quickly achieve a high level of accuracy. Finally, we deploy the model in production, where you can start generating predictions to achieve the best results. When finished, you will understand how Amazon SageMaker removes the complexity and barriers that typically slow down developers using ML.
Artifical Intelligence and Machine Learning 201, AWS Federal Pop-Up LoftAmazon Web Services
Come join us for a one-day session where you will learn about the science of computer vision (CV) and train custom CV models utilizing Amazon SageMaker. In this course, you'll learn about Amazon's managed machine learning platform and utilize publicly available real-world ground truth data sets to train models leveraging the built-in ML algorithms of Amazon SageMaker to detect objects and buildings. This is a hands-on workshop, attendees should bring your own laptops.
Building Machine Learning inference pipelines at scale | AWS Summit Tel Aviv ...AWS Summits
Real-life Machine Learning (ML) workloads typically require more than training and predicting: data often needs to be pre-processed and post-processed, sometimes in multiple steps. Thus, developers and data scientists have to train and deploy not just a single algorithm, but a sequence of algorithms that will collaborate in delivering predictions from raw data. In this session, we’ll first show you how to use Apache Spark MLlib to build ML pipelines, and we’ll discuss scaling options when datasets grow huge. We’ll then show how to how implement inference pipelines on Amazon SageMaker, using Apache Spark, Scikit-learn, as well as ML algorithms implemented by Amazon.
Accelerate_Digital_Transformation_through_AI-powered_Cloud_Analytics_Moderniz...Amazon Web Services
Modernizing your analytics capabilities to deliver rapid new insights is critical to successfully drive data-driven digital transformation. Many organizations find it challenging to connect, understand and deliver the right data to generate new insights. Learn about the latest patterns, solutions and benefits of Informatica's next-generation Enterprise Data Management platform to unleash the power of your data through the modern cloud data infrastructure of AWS. See how you can accelerate AI-driven next-generation analytics by cataloging and integrating structured and unstructured data from hundreds of data sources from multiple on-premises and cloud data sources.
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.
The document provides an overview of Amazon's machine learning capabilities including:
- Platform services like EC2 P3 instances and Deep Learning AMIs for training models
- Managed services like SageMaker for building, training, and deploying models, and applications services like Rekognition, Transcribe, Translate, and Comprehend for vision, speech and text analysis
- It describes how these capabilities are used across Amazon for applications like fulfilment, search, and developing new products
See how Public Sector organisations and AWS Partners are leveraging Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. By integrating learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Observe how to architect these solutions using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexas
Speaker: Craig Lawton, Smart Australia & IoT Specialist, AWS
AWS Summit Singapore 2019 | Accelerating ML Adoption with Our New AI servicesAmazon Web Services
Speaker: Ben Snively, Principal Solutions Architect - Data & Analytics, AWS
Note: This is part 2 of the deck.
Adding to the existing AI services, AWS continues to bridge the gap for developers to build ML solutions without the hurdle of having data science expertise. In this session learn about the new services announced at re: Invent (Forecast, Textract and Personalize) and get a preview of what to expect when building time series models, OCR and recommendation engines with little to no data science experience.
Adding intelligence to applications - AIM201 - Chicago AWS SummitAmazon Web Services
AI has already been integrated into many use cases, but we've just 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: 1) “voice of the customer” analytics to better understand what your customers are thinking and saying; 2) document analysis and processing to move beyond the limitations of traditional OCR; and 3) chatbots to improve in-app customer service and customer contact center experiences. We also discuss how to use AI in use cases within the media, healthcare, and financial services industries.
This document discusses Amazon SageMaker, an AWS service that helps address common machine learning workflow problems. It automates much of the infrastructure setup required for machine learning and provides pre-built algorithms, notebooks, processing resources and more. The document outlines typical machine learning workflows for development, training and inference, and how SageMaker addresses issues like environment setup, distributed training and model deployment. It also provides an overview of the SageMaker architecture and SDK development process.
Solve complex business problems with managed ML services.pdfAmazon Web Services
Personalization and forecasting have long been very complex problems to solve for organizations. In this session, we'll show you how to use Amazon Personalize and Amazon Forecast, two new services that let you create individualized recommendations for customers, and deliver highly accurate forecasts. Both run on fully-managed infrastructure, and provide easy-to-use recipes that deliver high-quality models even if you have little Machine Learning experience.
Amazon SageMaker Ground Truth: Build High-Quality and Accurate ML Training Da...Amazon Web Services
Successful machine learning models are built on high-quality training datasets. Labeling raw data to get accurate training datasets involves a lot of time and effort because sophisticated models can require thousands of labeled examples to learn from, before they can produce good results. Typically, the task of labeling is distributed across a large number of humans, adding significant overhead and cost. Join us as we introduce Amazon SageMaker Ground Truth, a new service that provides an effective solution to reduce this cost and complexity using a machine learning technique called active learning. Active learning reduces the time and manual effort required to do data labeling, by continuously training machine learning algorithms based on labels from humans. By iterating through ambiguous data points, Ground Truth improves the ability to automatically label data resulting in high-quality training datasets.
Level: 300
Speaker: Kris Skrinak - Partner Solutions Architect, ML Global Lead, AWS
Sviluppa, addestra e distribuisci modelli di machine learning.pdfAmazon Web Services
The document discusses Amazon SageMaker, an AWS managed service for building, training, and deploying machine learning models. It provides an overview of the key capabilities of SageMaker such as using built-in algorithms, bringing your own algorithms/containers, hyperparameter tuning, hosting models for inference, and batch transforms. It also discusses how SageMaker integrates with other AWS services like S3, EC2, and Marketplace.
Previously, ETL meant using proprietary products with commercial databases and users with specialist skills. Learn how to create ETL data pipelines that can securely consume data at scale while using open source technologies and languages to enable your organisation, team, and data.
Speaker: Paul Macey, Big Data Specialist, AWS
Bonus-Session-Interledger-DvP-Settlement-on-Amazon-Managed-BlockchainAmazon Web Services
Project Ubin is a collaborative project first launched in 2016 by the Monetary Authority of Singapore (MAS) and the Association of Banks in Singapore (ABS). In August 2018, MAS and SGX announced partnership with Anquan, Deloitte and Nasdaq to harness blockchain technology for settlement of tokenised assets. In order to explore the possible DvP settlement models, six blockchains of varied capabilities and features were used in prototypes. In this session, Peter from SGX will offer deeper insights into a DvP prototype developed with Deloitte that was hosted on Amazon Managed Blockchain, and will also discuss potential future applications of DLT (Distributed Ledger Technology) for capital markets, while Michael from AWS will discuss some of the common patterns seen in blockchain networks as well as features that control the privacy of data on shared blockchain networks.
AIM301 - Breaking Language Barriers With AI - Tel Aviv Summit 2019Boaz Ziniman
AI and Machine learning allow developers to introduce new language capabilities in their apps and use Natural Language Processing and Natural Language Understanding to break language barriers, add new functionality and expand their target audience. This session will focus on several AWS AI services for developers, that allow you to add such functionality to your code with minimal effort. We will build an automatic translator, interact with text to speech and try to extract sentiments from live text coming from different feeds.
In this presentation, we will explore three different ways you can build backends for real-time applications. First, we'll examine how to run and scale SignalR and other similar opensource frameworks on AWS using Amazon ElastiCache. Next, we will take a look at the new WebSocket support in API gateway and discover how it enables us to convert Lambda functions into real-time solutions. Finally, we will look at AppSyncs incredible real-time support for web and mobile applications
Generational Shifts and customer expectations has greatly changed the way insurance works, affecting insurer's channel, product and brand strategies. New players ike virtual insurers are getting ahead in the game. In this session, Bowtie, the first virtual insurer in Hong Kong will dive deep into how they leverage the AWS cloud technologies to build a new operations model, accelerate their business and minimize capital investment.
Performing real-time ETL into data lakes - ADB202 - Santa Clara AWS Summit.pdfAmazon Web Services
In this session, we discuss several options for performing real-time extract, transform, and load (ETL) using Amazon Kinesis, AWS Lambda, AWS Glue, and Amazon S3. We provide an overview of the different options that have distinct advantages in building real-time ETL applications before loading a data lake or warehouse.
Build, train, and deploy ML models with Amazon SageMaker - AIM302 - New York ...Amazon 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 models at any scale. In this workshop, you learn how to build an ML model using Amazon SageMaker's built-in algorithms and frameworks. We then train the model using automatic model tuning to quickly achieve a high level of accuracy. Finally, we deploy the model in production, where you can start generating predictions to achieve the best results. When finished, you will understand how Amazon SageMaker removes the complexity and barriers that typically slow down developers using ML.
Artifical Intelligence and Machine Learning 201, AWS Federal Pop-Up LoftAmazon Web Services
Come join us for a one-day session where you will learn about the science of computer vision (CV) and train custom CV models utilizing Amazon SageMaker. In this course, you'll learn about Amazon's managed machine learning platform and utilize publicly available real-world ground truth data sets to train models leveraging the built-in ML algorithms of Amazon SageMaker to detect objects and buildings. This is a hands-on workshop, attendees should bring your own laptops.
Building Machine Learning inference pipelines at scale | AWS Summit Tel Aviv ...AWS Summits
Real-life Machine Learning (ML) workloads typically require more than training and predicting: data often needs to be pre-processed and post-processed, sometimes in multiple steps. Thus, developers and data scientists have to train and deploy not just a single algorithm, but a sequence of algorithms that will collaborate in delivering predictions from raw data. In this session, we’ll first show you how to use Apache Spark MLlib to build ML pipelines, and we’ll discuss scaling options when datasets grow huge. We’ll then show how to how implement inference pipelines on Amazon SageMaker, using Apache Spark, Scikit-learn, as well as ML algorithms implemented by Amazon.
Accelerate_Digital_Transformation_through_AI-powered_Cloud_Analytics_Moderniz...Amazon Web Services
Modernizing your analytics capabilities to deliver rapid new insights is critical to successfully drive data-driven digital transformation. Many organizations find it challenging to connect, understand and deliver the right data to generate new insights. Learn about the latest patterns, solutions and benefits of Informatica's next-generation Enterprise Data Management platform to unleash the power of your data through the modern cloud data infrastructure of AWS. See how you can accelerate AI-driven next-generation analytics by cataloging and integrating structured and unstructured data from hundreds of data sources from multiple on-premises and cloud data sources.
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.
The document provides an overview of Amazon's machine learning capabilities including:
- Platform services like EC2 P3 instances and Deep Learning AMIs for training models
- Managed services like SageMaker for building, training, and deploying models, and applications services like Rekognition, Transcribe, Translate, and Comprehend for vision, speech and text analysis
- It describes how these capabilities are used across Amazon for applications like fulfilment, search, and developing new products
See how Public Sector organisations and AWS Partners are leveraging Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. By integrating learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Observe how to architect these solutions using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexas
Speaker: Craig Lawton, Smart Australia & IoT Specialist, AWS
AWS Summit Singapore 2019 | Accelerating ML Adoption with Our New AI servicesAmazon Web Services
Speaker: Ben Snively, Principal Solutions Architect - Data & Analytics, AWS
Note: This is part 2 of the deck.
Adding to the existing AI services, AWS continues to bridge the gap for developers to build ML solutions without the hurdle of having data science expertise. In this session learn about the new services announced at re: Invent (Forecast, Textract and Personalize) and get a preview of what to expect when building time series models, OCR and recommendation engines with little to no data science experience.
AWS Data-Driven Insights Learning Series_ANZ Sep 2019 Part 2Amazon Web Services
AWS has been supporting companies across Australia and New Zealand to put their most innovative tools and technologies to work to achieve their business needs and goals. AWS and our ecosystem of partners has helped the likes of CP Mining, IntelliHQ, WesCEF, Oz Minerals, Woodside and many more to modernise their analytics and data architecture in order to successfully generate business value from their data.
This event series aimed to educate customers with a broader understanding of how to build next-gen data lakes and analytics platforms and make connections with AWS.
Building Next Generation Cybersecurity with Today's Machine Learning SolutionsAmazon Web Services
Go beyond cyber basics and learn how to enable threat detection to proactively monitor and get ahead of unusual user behaviors, account anomalies, and even data breaches. Leverage AI/ML to quickly and accurately assess your organization’s vulnerabilities without human intervention, and build a better cyber strategy that's ready for anything.
The document discusses Amazon Web Services' (AWS) efforts to democratize artificial intelligence (AI). It summarizes AWS's mission to put machine learning in the hands of every developer. It then describes some of AWS's AI services like Amazon SageMaker and Amazon Rekognition, and how customers are using these services.
This document discusses building a data lake on AWS. It notes that organizations that successfully generate value from data will outperform competitors. It outlines challenges of data visibility, multiple access mechanisms, and analyzers needing access. AWS is presented as the perfect solution with its storage, analysis and security capabilities at scale. Case studies of Celgene and IEP are presented that used AWS for their data lakes. Traditional analytics are separated from data warehousing, but data lakes extend this by including diverse data and analytical engines at larger scale with lower costs. The AWS portfolio for data lakes, analytics and IoT is presented as the most complete toolset. Building value from the data lake is discussed through machine learning, analytics, data movement and visualization.
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.
This workshop will walk you trough building a serverless website, powered by AWS AI services, as part of the website backend.We will deploy a website on S3, use API Gateway and Lambda as our backend and integrate Amazon Rekognition to enrich user generated content.
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.
Build Machine Learning Models with Amazon SageMaker (April 2019)Julien SIMON
The document discusses 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 algorithms and notebooks, tools for data labeling and preparation, one-click training and tuning of models, and deployment of trained models into production. The document also provides examples of using SageMaker for tasks like image classification and text analysis.
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"
AWS Summit Singapore 2019 | Big Data Analytics Architectural Patterns and Bes...AWS Summits
Speaker: Renee Lo, Head of Big Data, Analytics, and AI, ASEAN, AWS
Customer Speaker: Natalia Kozyura, Head of Innovation Center, FWD Group
We discuss architectural principles that simplify big data analytics. We'll apply these principles to various stages of big data processing: collect, store, process, analyse, and visualise. We'll discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Modern Data Platforms - Thinking Data Flywheel on the CloudAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Modern Data Platforms - Thinking Data Flywheel on the Cloud
Speaker:
Roy Ben-Alta, AWS
For more Alluxio events: https://www.alluxio.io/events/
The document discusses machine learning at the edge for industrial applications. It describes an AIoT (artificial intelligence of things) lifecycle that includes transporting and routing data, aggregating and processing data in the cloud, training machine learning models in the cloud, and deploying models for inference at the edge. It provides examples of using AWS IoT Greengrass to deploy models to edge devices for on-device inference to derive intelligence and outcomes locally.
This document provides an agenda and overview for an MLOps workshop hosted by Amazon Web Services. The agenda includes introductions to Amazon AI, MLOps, Amazon SageMaker, machine learning pipelines, and a hands-on exercise to build an MLOps pipeline. It discusses key concepts like personas in MLOps, the CRISP-DM process, microservices deployment, and challenges of MLOps. It also provides overviews of Amazon SageMaker for machine learning and AWS services for continuous integration/delivery.
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.
Machine learning at the edge for industrial applications - SVC302 - New York ...Amazon Web Services
In this talk, learn how you can integrate edge computing and machine learning with industrial IoT solutions by combining AWS Cloud services with AWS IoT Greengrass. We then discover how machine learning can provide important functions in mixed criticality systems through practical machine learning examples at the edge with AWS IoT Greengrass on Zynq Ultrascale+ and Amazon FreeRTOS on Xilinx Zynq-7000. You will see how this is applied across object classification, model-based calibration, and model-predictive control inferencing.
Similar to AI/ML Week: Strengthen Cybersecurity (20)
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.