Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders can understand and support, and help create the right conditions for delivering successful ML-based solutions to your citizens. Understand AWS ML and AI services while relating to your specific requirements.
Speakers:
Manav Sehgal, Head of Solutions Architecture, AISPL
Atanu Roy, Specialist Solutions Architect, AISPL
Moving backups to the cloud and managing data protection across on-premises and cloud environments can be challenging. Veeam enables data protection and portability to the AWS Cloud with enterprise-class backup and disaster recovery for Amazon EC2, Amazon RDS, DynamoDB, and Amazon EBS.
Join us to learn how to backup, restore, and protect both on-premises and AWS instances with Veeam Availability Solutions and the new Veeam Cloud Tier, which integrates native Amazon S3 object storage support into Veeam's flagship product Availability Suite.
Speaker: Shuja Mirza, Head of Pre-sales, India & SAARC, Veeam
This document summarizes and promotes several Amazon Web Services (AWS) machine learning and artificial intelligence services, including Amazon Personalize, Amazon Forecast, Amazon Textract, Amazon Rekognition, Amazon Comprehend, Amazon Polly, Amazon Lex, and Amazon Transcribe. It provides high-level descriptions of each service and how they can be used to add capabilities like personalization, forecasting, text/data extraction from documents, image and video analysis, natural language processing, speech synthesis, and speech recognition to applications without requiring machine learning expertise.
Move users to AWS with Amazon WorkSpaces and Amazon AppStream 2-0Amazon Web Services
The document discusses Amazon Web Services end user computing solutions including Amazon WorkDocs, Amazon WorkLink, Amazon WorkSpaces, and Amazon AppStream 2.0. It provides examples of how customers can use each solution to provide secure access to files, applications and desktops from any device. It also includes a case study of how one customer implemented Amazon WorkSpaces and was able to save costs.
AWS Summit Milano 2019 - Creare e gestire Data Lake e Data Warehouses - Giorgio Nobile, Solutions Architect, AWS | Francesco Marelli, Solutions Architect, AWS | Cliente: THRON
This document discusses how AWS can help improve manufacturing operations through digital transformation and industrial IoT. It begins by outlining key industry trends and challenges facing manufacturing, such as emerging markets, complex supply chains, demanding customers, and workforce issues. It then provides examples of how manufacturers are using AWS services like IoT, machine learning and analytics to gain insights from data, optimize operations, improve quality and efficiency, and protect intellectual property. Specific use cases discussed include predictive maintenance, quality control, and asset monitoring. The reference architecture shows how AWS services integrate with existing IT and OT systems to securely connect devices at the edge to applications in the cloud.
Deriving Value with Next Gen Analytics and ML ArchitecturesAmazon Web Services
This presentation was delivered on March 19, 2019at Gartner's Data and Analytics Summit in Orlando, FL. Rahul Pathak, GM at AWS discusses Deriving Value with Next Gen Analytics and ML Architectures on AWS.
The People Pillar of Cloud Adoption: Developing Your Workforce & Building Dig...Amazon Web Services
A successful cloud-transformation journey incorporates three pillars: people, process, and technology. Far too often, organisations focus on process improvements and technology implementation, but ignore the human aspect. Many leaders acknowledge that the first two are easy to modify, while influencing culture is more difficult. This session covers best-practice methods meant to empower customers to address this challenge. Learn about roles and responsibilities germane to the transition and post-cloud adoption phase. Assess your organisation’s gaps among the requisite skills and competencies, build effective training models, and shape an effective DevOps culture.
When it comes to building our own services, our engineering groups have strong opinions, and they express them in the technologies they pick: Are microservices always the way to go? Should we choose serverless, containers, or serverless containers? Is relational over? Is Java over? Learn about our experience in building AWS services and working with customers on their cloud-native apps.
Moving backups to the cloud and managing data protection across on-premises and cloud environments can be challenging. Veeam enables data protection and portability to the AWS Cloud with enterprise-class backup and disaster recovery for Amazon EC2, Amazon RDS, DynamoDB, and Amazon EBS.
Join us to learn how to backup, restore, and protect both on-premises and AWS instances with Veeam Availability Solutions and the new Veeam Cloud Tier, which integrates native Amazon S3 object storage support into Veeam's flagship product Availability Suite.
Speaker: Shuja Mirza, Head of Pre-sales, India & SAARC, Veeam
This document summarizes and promotes several Amazon Web Services (AWS) machine learning and artificial intelligence services, including Amazon Personalize, Amazon Forecast, Amazon Textract, Amazon Rekognition, Amazon Comprehend, Amazon Polly, Amazon Lex, and Amazon Transcribe. It provides high-level descriptions of each service and how they can be used to add capabilities like personalization, forecasting, text/data extraction from documents, image and video analysis, natural language processing, speech synthesis, and speech recognition to applications without requiring machine learning expertise.
Move users to AWS with Amazon WorkSpaces and Amazon AppStream 2-0Amazon Web Services
The document discusses Amazon Web Services end user computing solutions including Amazon WorkDocs, Amazon WorkLink, Amazon WorkSpaces, and Amazon AppStream 2.0. It provides examples of how customers can use each solution to provide secure access to files, applications and desktops from any device. It also includes a case study of how one customer implemented Amazon WorkSpaces and was able to save costs.
AWS Summit Milano 2019 - Creare e gestire Data Lake e Data Warehouses - Giorgio Nobile, Solutions Architect, AWS | Francesco Marelli, Solutions Architect, AWS | Cliente: THRON
This document discusses how AWS can help improve manufacturing operations through digital transformation and industrial IoT. It begins by outlining key industry trends and challenges facing manufacturing, such as emerging markets, complex supply chains, demanding customers, and workforce issues. It then provides examples of how manufacturers are using AWS services like IoT, machine learning and analytics to gain insights from data, optimize operations, improve quality and efficiency, and protect intellectual property. Specific use cases discussed include predictive maintenance, quality control, and asset monitoring. The reference architecture shows how AWS services integrate with existing IT and OT systems to securely connect devices at the edge to applications in the cloud.
Deriving Value with Next Gen Analytics and ML ArchitecturesAmazon Web Services
This presentation was delivered on March 19, 2019at Gartner's Data and Analytics Summit in Orlando, FL. Rahul Pathak, GM at AWS discusses Deriving Value with Next Gen Analytics and ML Architectures on AWS.
The People Pillar of Cloud Adoption: Developing Your Workforce & Building Dig...Amazon Web Services
A successful cloud-transformation journey incorporates three pillars: people, process, and technology. Far too often, organisations focus on process improvements and technology implementation, but ignore the human aspect. Many leaders acknowledge that the first two are easy to modify, while influencing culture is more difficult. This session covers best-practice methods meant to empower customers to address this challenge. Learn about roles and responsibilities germane to the transition and post-cloud adoption phase. Assess your organisation’s gaps among the requisite skills and competencies, build effective training models, and shape an effective DevOps culture.
When it comes to building our own services, our engineering groups have strong opinions, and they express them in the technologies they pick: Are microservices always the way to go? Should we choose serverless, containers, or serverless containers? Is relational over? Is Java over? Learn about our experience in building AWS services and working with customers on their cloud-native apps.
Serverless architectures let you build and deploy applications and services with infrastructure resources that require zero administration. In the past, you had to provision and scale servers to run your application code, install and operate distributed databases, and build and run custom software to handle API requests. Now, AWS provides a stack of scalable, fully-managed services that eliminates these operational complexities. In this session, you will learn about serverless architectures, their benefits, and the basics of the AWS’s serverless stack (e.g., AWS Lambda, Amazon API Gateway, and AWS Step Functions). You will also get practical tips and tricks, best practices, and architecture patterns that you can take back and implement immediately.
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.
Using ML to detect and prevent fraud without compromising user experience - F...Amazon Web Services
Based on NuData Security’s experience analyzing internet traffic, the company estimates that more than half is fake, which translates into significant fraudulent activity around login and authentication. Unfortunately, conventional username/password-based authentication is unreliable, and multifactor authentication adds user friction. Is there a way to solve account takeover, automated attacks, and known-user recognition without compromising user experience? NuData has taken a cloud-native approach to detecting identity threats and preventing fraud. Learn how the company built an AI/ML-powered platform that enables rapid response to an ever-changing attack landscape and accelerates innovation to crush attack vectors at any scale.
Amazon digital user engagement solutions - SVC221 - New York AWS SummitAmazon Web Services
In this session, we describe how AWS provides the Amazon customer-centric culture of innovation, key technology building blocks, and a user engagement platform to help companies better engage their users. You also learn how Disney’s streaming service is utilizing the Amazon approach to engage its users. The intended audience for this session includes developers and business professionals who are responsible for digitally transforming their company.
Migrating Data to the Cloud: Explore Your Options From AWSAmazon Web Services
AWS offers a variety of data migration services and tools to facilitate moving gigabytes to petabytes of data using your networks, our networks, or even email. Learn about the available data migration options, including the AWS Snowball family, AWS Storage Gateway, Amazon S3 Transfer Acceleration, and other approaches. We provide the guidance to help you find the right service or tool to fit your requirements, and share relevant customers use cases to inspire your first steps with the cloud.
The document discusses Enexis' migration journey to AWS. It provides background on Enexis' previous IT environment and challenges, including an outsourced and inefficient process. It then outlines Enexis' goals for the migration, including being ready for energy industry changes, reliability, cost reduction, and accelerating the energy transition. The solution involved building a Cloud Native Architecture Platform (CNAP) on AWS to automate processes and provide protection against mistakes.
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.
Accelerating_Enterprise_Cloud_Transformation_By_Master_IT_ComplexityAmazon Web Services
Adoption of cloud native architecture and multi-cloud services become the strategic path for competitive advantage. While the dynamic and complexity nature of the new platform introduce risks to realize the values, join us to see how our third generation software intelligence platform paves the way for autonomous cloud management, which maximize the values of your cloud investment.
AWS Analytics Services - When to use what? | AWS Summit Tel Aviv 2019AWS Summits
AWS provides a wide range of data analytics tools with the power to analyze vast volumes of customer, business, and transactional data quickly and at low cost.
In this session, we provide an overview of AWS analytics services and discuss how customers are using these services today. We will also discuss the new database and analytics services and features we launched in the last year.
How SAP customers are benefiting from machine learning and IoT with AWS - MAD...Amazon Web Services
The document discusses how SAP customers are extending what's possible by using AWS services. It provides an overview of the SAP and AWS alliance and timeline of milestones. It then discusses how SAP customers on AWS are categorized into four categories related to migrating, modernizing, managing operations, and innovating. The rest of the document discusses specific AWS services that can help with apps/APIs, big data/analytics, IoT, DevOps, and machine learning for SAP customers.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders and senior management can understand and support, and help create the right conditions for delivering successful ML-based solutions to your business.
The document discusses machine learning at the edge for industrial applications. It describes an AIoT (Artificial Intelligence of Things) lifecycle that includes data transport and routing, data aggregation and processing, machine learning model generation in the cloud, and model inference at the edge. It provides examples of using AWS services like IoT, SageMaker, and Greengrass in an industrial IoT architecture. The presentation also covers topics like developing and deploying models on edge devices and integrating machine learning into mixed criticality systems.
Next generation intelligent data lakes, powered by GraphQL & AWS AppSync - MA...Amazon Web Services
GraphQL is a query language for APIs and a runtime to fulfill these queries, allowing applications to easily connect and access data stored on any type of database technology or API. AWS AppSync provides a powerful and flexible serverless GraphQL API that securely accesses, manipulates, and combines data from multiple sources at any scale, enabling you to build any kind of application on a range of data sources independently of the underlying database technology. In this session, we discuss different use cases where AWS AppSync and GraphQL power next-generation applications. Special guest, Candid Partners, shares how it uses AWS AppSync in its Data Fabric solution to simplify large-scale data management using a GraphQL API to interact with data lakes.
Need for Speed – Intro To Real-Time Data Streaming Analytics on AWS | AWS Sum...AWS Summits
AWS provides multiple ways to ingest and process real-time data generated from sources such as Edge device, logs, websites, mobile apps, IoT devices and more.
In this session we will compare the different tools and technologies and share best practices for when to use what.
The session will cover: Apache Kafka, Kinesis Data Streams/Firehose, MSK (Managed Kafka), Kinesis Data Analytics for SQL and Java (Flink), Apache Spark and more.
Continuous Integration and Continuous Delivery Best Practices for Building Mo...Amazon Web Services
Continuous integration and continuous delivery (CI/CD) techniques enable teams to increase agility and expedite the release of high-quality products. In this talk, we walk you through best practices for building CI/CD workflows that enable you to manage your serverless and containerized applications. We cover infrastructure as code application models, such as the AWS Serverless Application Model, as well as how to set up CI/CD release pipelines with AWS CodePipeline and AWS CodeBuild. Finally, we show you how to automate safer deployments with AWS CodeDeploy.
Data warehouses (DWs) are central repositories of integrated data from one or more disparate sources, used for reporting, data analysis, and business intelligence. In this session, dive deep into concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS.
Additionally, we will review patterns in hybrid environments for collecting, storing, and preparing data for DWs using Amazon DynamoDB, AWS Database Migration Service (AWS DMS), Amazon Kinesis Data Firehose, and Amazon Simple Storage Service (Amazon S3). Learn how our customers have successfully migrated from on-premises DW to Amazon Redshift to achieve their goals by implementing parallel query execution, key performance adjustments, and more.
The document discusses AWS machine learning and Amazon SageMaker. It highlights that AWS provides the broadest and deepest set of AI and ML services, including over 200 new features launched in the last year. It promotes Amazon SageMaker as a fully managed service that makes machine learning accessible to developers and allows them to build, train, and deploy models with one click. The document also provides examples of how various companies use AWS ML services to improve customer experiences, security, media workflows, and more.
Alexa transformed the smart home market segment and is now transforming how we interact with applications and technology at work. In this session, learn about how Alexa is an example of Conversational AI in Education.
Serverless architectures let you build and deploy applications and services with infrastructure resources that require zero administration. In the past, you had to provision and scale servers to run your application code, install and operate distributed databases, and build and run custom software to handle API requests. Now, AWS provides a stack of scalable, fully-managed services that eliminates these operational complexities. In this session, you will learn about serverless architectures, their benefits, and the basics of the AWS’s serverless stack (e.g., AWS Lambda, Amazon API Gateway, and AWS Step Functions). You will also get practical tips and tricks, best practices, and architecture patterns that you can take back and implement immediately.
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.
Using ML to detect and prevent fraud without compromising user experience - F...Amazon Web Services
Based on NuData Security’s experience analyzing internet traffic, the company estimates that more than half is fake, which translates into significant fraudulent activity around login and authentication. Unfortunately, conventional username/password-based authentication is unreliable, and multifactor authentication adds user friction. Is there a way to solve account takeover, automated attacks, and known-user recognition without compromising user experience? NuData has taken a cloud-native approach to detecting identity threats and preventing fraud. Learn how the company built an AI/ML-powered platform that enables rapid response to an ever-changing attack landscape and accelerates innovation to crush attack vectors at any scale.
Amazon digital user engagement solutions - SVC221 - New York AWS SummitAmazon Web Services
In this session, we describe how AWS provides the Amazon customer-centric culture of innovation, key technology building blocks, and a user engagement platform to help companies better engage their users. You also learn how Disney’s streaming service is utilizing the Amazon approach to engage its users. The intended audience for this session includes developers and business professionals who are responsible for digitally transforming their company.
Migrating Data to the Cloud: Explore Your Options From AWSAmazon Web Services
AWS offers a variety of data migration services and tools to facilitate moving gigabytes to petabytes of data using your networks, our networks, or even email. Learn about the available data migration options, including the AWS Snowball family, AWS Storage Gateway, Amazon S3 Transfer Acceleration, and other approaches. We provide the guidance to help you find the right service or tool to fit your requirements, and share relevant customers use cases to inspire your first steps with the cloud.
The document discusses Enexis' migration journey to AWS. It provides background on Enexis' previous IT environment and challenges, including an outsourced and inefficient process. It then outlines Enexis' goals for the migration, including being ready for energy industry changes, reliability, cost reduction, and accelerating the energy transition. The solution involved building a Cloud Native Architecture Platform (CNAP) on AWS to automate processes and provide protection against mistakes.
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.
Accelerating_Enterprise_Cloud_Transformation_By_Master_IT_ComplexityAmazon Web Services
Adoption of cloud native architecture and multi-cloud services become the strategic path for competitive advantage. While the dynamic and complexity nature of the new platform introduce risks to realize the values, join us to see how our third generation software intelligence platform paves the way for autonomous cloud management, which maximize the values of your cloud investment.
AWS Analytics Services - When to use what? | AWS Summit Tel Aviv 2019AWS Summits
AWS provides a wide range of data analytics tools with the power to analyze vast volumes of customer, business, and transactional data quickly and at low cost.
In this session, we provide an overview of AWS analytics services and discuss how customers are using these services today. We will also discuss the new database and analytics services and features we launched in the last year.
How SAP customers are benefiting from machine learning and IoT with AWS - MAD...Amazon Web Services
The document discusses how SAP customers are extending what's possible by using AWS services. It provides an overview of the SAP and AWS alliance and timeline of milestones. It then discusses how SAP customers on AWS are categorized into four categories related to migrating, modernizing, managing operations, and innovating. The rest of the document discusses specific AWS services that can help with apps/APIs, big data/analytics, IoT, DevOps, and machine learning for SAP customers.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders and senior management can understand and support, and help create the right conditions for delivering successful ML-based solutions to your business.
The document discusses machine learning at the edge for industrial applications. It describes an AIoT (Artificial Intelligence of Things) lifecycle that includes data transport and routing, data aggregation and processing, machine learning model generation in the cloud, and model inference at the edge. It provides examples of using AWS services like IoT, SageMaker, and Greengrass in an industrial IoT architecture. The presentation also covers topics like developing and deploying models on edge devices and integrating machine learning into mixed criticality systems.
Next generation intelligent data lakes, powered by GraphQL & AWS AppSync - MA...Amazon Web Services
GraphQL is a query language for APIs and a runtime to fulfill these queries, allowing applications to easily connect and access data stored on any type of database technology or API. AWS AppSync provides a powerful and flexible serverless GraphQL API that securely accesses, manipulates, and combines data from multiple sources at any scale, enabling you to build any kind of application on a range of data sources independently of the underlying database technology. In this session, we discuss different use cases where AWS AppSync and GraphQL power next-generation applications. Special guest, Candid Partners, shares how it uses AWS AppSync in its Data Fabric solution to simplify large-scale data management using a GraphQL API to interact with data lakes.
Need for Speed – Intro To Real-Time Data Streaming Analytics on AWS | AWS Sum...AWS Summits
AWS provides multiple ways to ingest and process real-time data generated from sources such as Edge device, logs, websites, mobile apps, IoT devices and more.
In this session we will compare the different tools and technologies and share best practices for when to use what.
The session will cover: Apache Kafka, Kinesis Data Streams/Firehose, MSK (Managed Kafka), Kinesis Data Analytics for SQL and Java (Flink), Apache Spark and more.
Continuous Integration and Continuous Delivery Best Practices for Building Mo...Amazon Web Services
Continuous integration and continuous delivery (CI/CD) techniques enable teams to increase agility and expedite the release of high-quality products. In this talk, we walk you through best practices for building CI/CD workflows that enable you to manage your serverless and containerized applications. We cover infrastructure as code application models, such as the AWS Serverless Application Model, as well as how to set up CI/CD release pipelines with AWS CodePipeline and AWS CodeBuild. Finally, we show you how to automate safer deployments with AWS CodeDeploy.
Data warehouses (DWs) are central repositories of integrated data from one or more disparate sources, used for reporting, data analysis, and business intelligence. In this session, dive deep into concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS.
Additionally, we will review patterns in hybrid environments for collecting, storing, and preparing data for DWs using Amazon DynamoDB, AWS Database Migration Service (AWS DMS), Amazon Kinesis Data Firehose, and Amazon Simple Storage Service (Amazon S3). Learn how our customers have successfully migrated from on-premises DW to Amazon Redshift to achieve their goals by implementing parallel query execution, key performance adjustments, and more.
The document discusses AWS machine learning and Amazon SageMaker. It highlights that AWS provides the broadest and deepest set of AI and ML services, including over 200 new features launched in the last year. It promotes Amazon SageMaker as a fully managed service that makes machine learning accessible to developers and allows them to build, train, and deploy models with one click. The document also provides examples of how various companies use AWS ML services to improve customer experiences, security, media workflows, and more.
Alexa transformed the smart home market segment and is now transforming how we interact with applications and technology at work. In this session, learn about how Alexa is an example of Conversational AI in Education.
In this session, we give an overview of the Amazon artificial intelligence and machine learning stack and discuss how it can help improve student outcomes, increase enrollment, open up new business models, optimize the admissions process, personalize learning for students, enhance engagement, improve the campus experience, and more.
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.
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019AWS Summits
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019Amazon Web Services
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
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.
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.
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.
AWS Summit Singapore 2019 | Build, Train and Deploy Deep Learning Models on A...AWS Summits
Speaker: Pedro Paez, Specialist Solutions Architect, AWS
Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform.
Amazon SageMaker - ML for every developer & data scientist ft. Workday - AIM2...Amazon Web Services
The document discusses Amazon SageMaker, AWS's platform for building, training, and deploying machine learning models at scale. It highlights key SageMaker capabilities like pre-built notebooks, built-in algorithms, one-click training on high-performance infrastructure, model optimization, and one-click deployment. It also discusses other AWS machine learning services like Ground Truth for data labeling, AWS Marketplace for accessing algorithms and models, and SageMaker Neo for optimized model deployment.
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.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders and senior management can understand and support, and help create the right conditions for delivering successful ML-based solutions to your business.
The document discusses Amazon Web Services' machine learning and AI services. It notes that AWS aims to put machine learning in the hands of every developer through its broad range of AI services like Amazon Rekognition, Amazon Transcribe, and Amazon Translate. It also discusses AWS' machine learning infrastructure and services that allow customers to build, train, and deploy machine learning models. Several examples are provided of customers using AWS AI services to automate processes, improve customer experiences, and more.
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.
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.
Similar to Drive Digital Transformation using Machine Learning (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.