By Hagay Lupesko, SDM, AWS
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
Presentacion utilizada durante Segurinfo 2014 - Rosario, Santa Fe, Argentina.
El objetivo es discutir sobre las amenazas que enfrentan las organizaciones y los sistemas que soportan el negocio, de forma de considerarlas dentro del plan de protección y mitigación de riesgos
by Cameron Worrell, Sr. Solutions Architect, AWS
In this talk, we will introduce several methods of threat detection and remediation on AWS, including GuardDuty, Macie, WAF, Shield, Lambda, AWS Config, Systems Manager and Inspector. We will do a brief overview of each of these services, and then talk about how to put them all together, to have a comprehensive thread detection and remediation solution. We will also discuss how to use these services across multiple AWS accounts and regions, to cover the governance needs of enterprise AWS deployments.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
Presentacion utilizada durante Segurinfo 2014 - Rosario, Santa Fe, Argentina.
El objetivo es discutir sobre las amenazas que enfrentan las organizaciones y los sistemas que soportan el negocio, de forma de considerarlas dentro del plan de protección y mitigación de riesgos
by Cameron Worrell, Sr. Solutions Architect, AWS
In this talk, we will introduce several methods of threat detection and remediation on AWS, including GuardDuty, Macie, WAF, Shield, Lambda, AWS Config, Systems Manager and Inspector. We will do a brief overview of each of these services, and then talk about how to put them all together, to have a comprehensive thread detection and remediation solution. We will also discuss how to use these services across multiple AWS accounts and regions, to cover the governance needs of enterprise AWS deployments.
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address different use cases and needs. This deck will help you to gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition, and learn about newly announced services Amazon Rekognition Video, Amazon Comprehend, Amazon Translate, and Amazon Transcribe. This presentation took place in Australia and New Zealand as part of the AWS Learning Series in 2018.
In this webinar, you'll learn about the foundational security blocks and how to start using them effectively to create robust and secure architectures. Discover how Identity and Access management is done and how it integrates with other AWS services. In addition, learn how to improve governance by using AWS Security Hub, AWS Config and CloudTrail to gain unprecedented visibility of activity in the account. Subsequently use AWS Config rules to rectify configuration issues quickly and effectively.
Security Incident Event Management
Real time monitoring of Servers, Network Devices.
Correlation of Events
Analysis and reporting of Security Incidents.
Threat Intelligence
Long term storage
Snyk Intro - Developer Security Essentials 2022Liran Tal
Overwhelmed with security issues in your Node.js applications? Not entirely sure how to write secure code? Join us in this workshop where you’ll learn how to improve security without being a security professional. We’ll use Snyk Code’s VS Code extension to catch and find security issues while you code, automatically fix security issues in your open source libraries, and see first-hand how to weaponize vulnerabilities to exploit working Node.js applications. You will also learn about the multiple ways of using Snyk to secure your projects, from the CLI, to CI/CD pipelines with GitHub Actions, and extend your know from secure code and secure dependencies to that of building secure containers to your Node.js apps on Docker.
Using AWS Control Tower to govern multi-account AWS environments at scale - G...Amazon Web Services
AWS Control Tower is a new AWS service that cloud administrators can use to set up and govern their secure, compliant, multi-account environments on AWS. In this session, we show you how Control Tower automates the creation of a secure and compliant landing zone with best-practice blueprints for a multi-account structure, identity and federated access management, a central log archive, cross-account security audits, and workflows for provisioning accounts with pre-approved configurations. We also discuss guardrails—pre-packaged governance rules created for security, operations, and compliance that you can apply enterprise-wide or to groups of accounts to enforce policies or detect violations. Finally, we show you how to easily manage and monitor all this through the Control Tower dashboard.
AWS Control Tower is a new AWS service that cloud administrators can use to set up and govern their secure, compliant, multi-account environments on AWS. In this session, we show you how Control Tower automates the creation of a secure and compliant landing zone with best-practice blueprints for a multi-account structure, identity and federated access management, a central log archive, cross-account security audits, and workflows for provisioning accounts with pre-approved configurations. We also discuss guardrails—pre-packaged governance rules created for security, operations, and compliance that you can apply enterprise-wide or to groups of accounts to enforce policies or detect violations. Finally, we show you how to easily manage and monitor all this through the Control Tower dashboard.
Threat Modeling as a structured activity for identifying and managing the objects (such as application) threats.
Threat Modeling – also called Architectural Risk Analysis is an essential step in the development of your application.
Without it, your protection is a shot in the dark
Introduction to the Well-Architected Framework and Tool - SVC208 - Anaheim AW...Amazon Web Services
Most modern businesses depend on a portfolio of technology solutions to operate and be successful every day. How do you know whether your team is following best practices or what the risks are in your architectures? This session shows how the AWS Well-Architected Framework provides prescriptive advice on best practices and how the AWS Well-Architected Tool enables you to measure and improve your technology portfolio. We explain how other customers are using AWS Well-Architected in their businesses, and we share what we learned from reviewing tens of thousands of architectures across operational excellence, security, reliability, performance efficiency, and cost optimization.
Train Models on Amazon SageMaker Using Data Not from Amazon S3 (AIM419) - AWS...Amazon Web Services
Questions often arise about training machine learning models using Amazon SageMaker with data from sources other than Amazon S3. In this chalk talk, we dive deep into training models in real time using data from Amazon DynamoDB or a relational database. We demonstrate how training models with Amazon SageMaker is quick and easy, regardless of the data source.
AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018Amazon Web Services
In this session, we discuss how to deploy a scalable environment that considers the AWS account structure, security services, network architecture, and user access. We present an overview of the AWS Landing Zone solution, an automated solution for setting up a robust and flexible AWS environment designed from the collective experience of AWS and our customers. The AWS Landing Zone helps automate the setup of a flexible account structure, security baseline, network structure, and user access based on best practices. Future growth is facilitated by an account vending machine component that simplifies the creation of additional accounts. Learn how the AWS Landing Zone can ensure that you start your AWS journey with the right foundation. We encourage you to attend the full AWS Landing Zone track, including SEC303. Search for #awslandingzone in the session catalog.
Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
CI/CD for Serverless and Containerized Applications (DEV309-R1) - AWS re:Inve...Amazon Web Services
To get the most out of the agility afforded by serverless and containers, it is essential to build CI/CD pipelines that help teams iterate on code and quickly release features. In this talk, we demonstrate how developers can build effective CI/CD release workflows to manage their serverless or containerized deployments on AWS. We cover infrastructure-as-code (IaC) application models, such as AWS Serverless Application Model (AWS SAM) and new imperative IaC tools. We also demonstrate how to set up CI/CD release pipelines with AWS CodePipeline and AWS CodeBuild, and we show you how to automate safer deployments with AWS CodeDeploy.
Kate Werling - Using Amazon SageMaker to build, train, and deploy your ML Mod...Amazon Web Services
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.
Level: 200-300
Speaker: Randall Hunt - Sr. Technical Evangelist, AWS
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address different use cases and needs. This deck will help you to gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition, and learn about newly announced services Amazon Rekognition Video, Amazon Comprehend, Amazon Translate, and Amazon Transcribe. This presentation took place in Australia and New Zealand as part of the AWS Learning Series in 2018.
In this webinar, you'll learn about the foundational security blocks and how to start using them effectively to create robust and secure architectures. Discover how Identity and Access management is done and how it integrates with other AWS services. In addition, learn how to improve governance by using AWS Security Hub, AWS Config and CloudTrail to gain unprecedented visibility of activity in the account. Subsequently use AWS Config rules to rectify configuration issues quickly and effectively.
Security Incident Event Management
Real time monitoring of Servers, Network Devices.
Correlation of Events
Analysis and reporting of Security Incidents.
Threat Intelligence
Long term storage
Snyk Intro - Developer Security Essentials 2022Liran Tal
Overwhelmed with security issues in your Node.js applications? Not entirely sure how to write secure code? Join us in this workshop where you’ll learn how to improve security without being a security professional. We’ll use Snyk Code’s VS Code extension to catch and find security issues while you code, automatically fix security issues in your open source libraries, and see first-hand how to weaponize vulnerabilities to exploit working Node.js applications. You will also learn about the multiple ways of using Snyk to secure your projects, from the CLI, to CI/CD pipelines with GitHub Actions, and extend your know from secure code and secure dependencies to that of building secure containers to your Node.js apps on Docker.
Using AWS Control Tower to govern multi-account AWS environments at scale - G...Amazon Web Services
AWS Control Tower is a new AWS service that cloud administrators can use to set up and govern their secure, compliant, multi-account environments on AWS. In this session, we show you how Control Tower automates the creation of a secure and compliant landing zone with best-practice blueprints for a multi-account structure, identity and federated access management, a central log archive, cross-account security audits, and workflows for provisioning accounts with pre-approved configurations. We also discuss guardrails—pre-packaged governance rules created for security, operations, and compliance that you can apply enterprise-wide or to groups of accounts to enforce policies or detect violations. Finally, we show you how to easily manage and monitor all this through the Control Tower dashboard.
AWS Control Tower is a new AWS service that cloud administrators can use to set up and govern their secure, compliant, multi-account environments on AWS. In this session, we show you how Control Tower automates the creation of a secure and compliant landing zone with best-practice blueprints for a multi-account structure, identity and federated access management, a central log archive, cross-account security audits, and workflows for provisioning accounts with pre-approved configurations. We also discuss guardrails—pre-packaged governance rules created for security, operations, and compliance that you can apply enterprise-wide or to groups of accounts to enforce policies or detect violations. Finally, we show you how to easily manage and monitor all this through the Control Tower dashboard.
Threat Modeling as a structured activity for identifying and managing the objects (such as application) threats.
Threat Modeling – also called Architectural Risk Analysis is an essential step in the development of your application.
Without it, your protection is a shot in the dark
Introduction to the Well-Architected Framework and Tool - SVC208 - Anaheim AW...Amazon Web Services
Most modern businesses depend on a portfolio of technology solutions to operate and be successful every day. How do you know whether your team is following best practices or what the risks are in your architectures? This session shows how the AWS Well-Architected Framework provides prescriptive advice on best practices and how the AWS Well-Architected Tool enables you to measure and improve your technology portfolio. We explain how other customers are using AWS Well-Architected in their businesses, and we share what we learned from reviewing tens of thousands of architectures across operational excellence, security, reliability, performance efficiency, and cost optimization.
Train Models on Amazon SageMaker Using Data Not from Amazon S3 (AIM419) - AWS...Amazon Web Services
Questions often arise about training machine learning models using Amazon SageMaker with data from sources other than Amazon S3. In this chalk talk, we dive deep into training models in real time using data from Amazon DynamoDB or a relational database. We demonstrate how training models with Amazon SageMaker is quick and easy, regardless of the data source.
AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018Amazon Web Services
In this session, we discuss how to deploy a scalable environment that considers the AWS account structure, security services, network architecture, and user access. We present an overview of the AWS Landing Zone solution, an automated solution for setting up a robust and flexible AWS environment designed from the collective experience of AWS and our customers. The AWS Landing Zone helps automate the setup of a flexible account structure, security baseline, network structure, and user access based on best practices. Future growth is facilitated by an account vending machine component that simplifies the creation of additional accounts. Learn how the AWS Landing Zone can ensure that you start your AWS journey with the right foundation. We encourage you to attend the full AWS Landing Zone track, including SEC303. Search for #awslandingzone in the session catalog.
Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
CI/CD for Serverless and Containerized Applications (DEV309-R1) - AWS re:Inve...Amazon Web Services
To get the most out of the agility afforded by serverless and containers, it is essential to build CI/CD pipelines that help teams iterate on code and quickly release features. In this talk, we demonstrate how developers can build effective CI/CD release workflows to manage their serverless or containerized deployments on AWS. We cover infrastructure-as-code (IaC) application models, such as AWS Serverless Application Model (AWS SAM) and new imperative IaC tools. We also demonstrate how to set up CI/CD release pipelines with AWS CodePipeline and AWS CodeBuild, and we show you how to automate safer deployments with AWS CodeDeploy.
Kate Werling - Using Amazon SageMaker to build, train, and deploy your ML Mod...Amazon Web Services
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.
Level: 200-300
Speaker: Randall Hunt - Sr. Technical Evangelist, AWS
Using Amazon SageMaker to build, train, & deploy your ML ModelsAmazon Web Services
Machine Learning Workshops at the San Francisco Loft
Build, Train, and Deploy ML Models Using SageMaker
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.
Level: 200-300
Speaker: Martin Schade - R&D Engineer, AWS Solutions Architecture
Build, Train, & Deploy ML Models Using SageMaker: Machine Learning Week San F...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Build, Train, and Deploy ML Models Using SageMaker
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.
Level: 200-300
Speaker: Amit Sharma - Principal Solutions Architect, AWS
Using Amazon SageMaker to build, train, and deploy your ML ModelsAmazon Web Services
by Neel Mitra, Solutions Architect, AWS
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.
AWS Machine Learning Week SF: Build, Train & Deploy ML Models Using SageMakerAmazon Web Services
AWS Machine Learning Week at the San Francisco Loft: Build, Train, and Deploy ML Models Using SageMaker
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.
Build, Train and Deploy ML Models using Amazon SageMakerHagay Lupesko
(presented in AWS ML Day in SF on June 2018)
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. This presentation goes over key use cases and features of SageMaker, including a hands-on demo of using SageMaker and MXNet to build, train and deploy a neural network for sentiment analysis.
Learning Objectives:
- Learn how Amazon SageMaker can be used for exploratory data analysis before training
- Learn how Amazon SageMaker provides managed distributed training with flexibility
- Learn how easy it is to deploy your models for hosting within Amazon SageMaker
Building Machine Learning models with Apache Spark and Amazon SageMaker | AWS...Amazon Web Services
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. In this session, we'll show you how to combine it with Apache Spark to build efficient Machine Learning pipeline.
Building a Recommender System Using Amazon SageMaker's Factorization Machine ...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Building a Recommender System Using Amazon SageMaker's Factorization Machine Algorithm
Factorization Machines are a powerful algorithm in the click prediction and recommendation space. Amazon SageMaker has a nearly infinitely scalable implementation that we'll show you how to use to build a recommender of your own.
Speaker: David Arpin - AI Platform Selections Leader, AI Platforms
Data Summer Conf 2018, “Build, train, and deploy machine learning models at s...Provectus
Machine learning often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and too slow. Amazon SageMaker 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. Apache MXNet and TensorFlow are pre-installed, and Amazon SageMaker offers a range of built-in, high-performance machine learning algorithms. If you want to train with an alternative framework or algorithm, you can bring your own in a Docker container.
Build, Train, and Deploy ML Models with Amazon SageMaker (AIM410-R2) - AWS re...Amazon Web Services
Come and help build the most accurate text classification model possible. A fully managed machine learning (ML) platform, Amazon SageMaker enables developers and data scientists to build, train, and deploy ML models using built-in or custom algorithms. In this workshop, you learn how to leverage Keras/TensorFlow deep learning frameworks to build a text classification solution using custom algorithms on Amazon SageMaker. You package custom training code in a Docker container, test it locally, and then use Amazon SageMaker to train a deep learning model. You then try to iteratively improve the model to achieve a higher level of accuracy. Finally, you deploy the model in production so different applications within the company can start leveraging this ML classification service. Please note that to actively participate in this workshop, you need an active AWS account with admin-level IAM permissions to Amazon SageMaker, Amazon Elastic Container Registry (Amazon ECR), and Amazon S3.
AWS Summit Singapore - Artificial Intelligence to Delight Your CustomersAmazon Web Services
Andrew Watts-Curnow, Senior Cloud Architect – Professional Services, APAC, AWS
Learn how advances in AI are enabling improvements in customer experience. This is a deep dive using machine learning frameworks for people who are familiar with building their own models. In this session, we will detail a facial recognition solution that can detect known customers and alert customer service staff.
End to End Model Development to Deployment using SageMakerAmazon Web Services
End to End Model Development to Deployment Using SageMaker
In this session we would be developing a model for image classification model (a convolutional neural network, or CNN). We would start off with some theory about CNNs, explore how they learn an image and then proceed towards hands-on lab. We would be using Amazon SageMaker to develop the model in Python, train the model and then to finally create an endpoint and run inference against it. We would be using a custom Conda Kernel for this exercise and would be looking at leveraging SageMaker features like LifeCycle Configurations to help us prepare the notebook before launch. Finally we would be deploying the model in production and run inference against it. We would also be able to monitor various parameters for endpoint performance such as endpoint’s CPU/Memory and Model inference performance metrics.
Level: 200-300
Machine Learning Models with Apache MXNet and AWS FargateAmazon Web Services
by Ahmad Khan, Sr. Solutions Architect, AWS
Deep Learning has been delivering state of the art results across a growing number of domains and use cases. Correspondingly, Deep Learning models are being deployed across a growing number of applications across segments. In this session, we will dive deep into serving machine learning models in production, and demonstrate how to efficiently deploy and serve models over serverless infrastructure using the open source project Model Server for Apache MXNet, Containers and AWS Fargate.
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.
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.
4. Amazon SageMaker
A fully-managed platform
that provides the quickest and easiest way for
data scientists and developers to get
ML models from idea to production.
5. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining
6. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining
7. Building
… or Apache Spark
through EMR and
the SageMaker
Spark SDK...
Use SageMaker‘s
hosted Notebook
Instances...
... or the Console
for a point and
click experience...
... or your own
device (EC2,
laptop, etc.)
8. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining
9. Training
Zero setup Streaming
datasets +
distributed
compute
Docker / ECS Deploy trained
models locally or to
Amazon SageMaker,
AWS Greengrass, AWS
DeepLens
10. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining
12. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining
13. Built-in algorithms
XGBoost, FM,
Linear, and
Forecasting
for supervised
learning
Kmeans, PCA,
and Word2Vec
for clustering
and pre-
processing
Image
classification
with
convolutional
neural networks
LDA and NTM
for topic
modeling,
seq2seq for
translation
14. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining
15. TensorFlow and Apache MXNet Docker Containers
… explore and
refine models in a
single Notebook
instance
… deploy to
production
Sample your
data… Use the same code
to train on the full
dataset in a cluster
of instances…
16. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining
17. Bring your own algorithm
... add algorithm
code to a Docker
container...
Pick your
preferred
framework...
... publish to ECS
Amazon ECS
18. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining
19. Hyperparameter Tuning
(Automated Model Tuning)
Run a large set of training
jobs with varying
hyperparameters...
... and search the
hyperparameter space for
improved accuracy.
20. Zero setup for data exploration
Resizable as you
need
Common tools
pre-installed
Easy access to
your data sources
No servers to
manage
22. Pay as you go and inexpensive
ML compute by the
second starting
at $0.0464/hr
ML storage by the
second
at $0.14
per GB-month
Data processed in
notebooks and hosting
at $0.016 per GB
Free trial to get started
quickly