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.
The document discusses a 4 phase approach to IT transformation with AWS: 1) Business alignment to define goals and roadmap, 2) Migration of existing applications, 3) Transforming operations to a new agile cloud model, and 4) Operating in the new state. Key activities include identifying measures of success, selecting a cloud adoption framework, developing a multi-year roadmap, establishing a cloud competency center, taking a phased migration approach, transforming the IT model, and providing constant education to the business. The outcomes discussed are examples of companies like Shell and Qantas successfully transforming parts of their operations on AWS.
Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AW...Amazon Web Services
This document discusses big data analytics architectural patterns and best practices. It covers collecting and storing data from various sources, processing and analyzing data using tools like Amazon Redshift, Amazon Athena and Amazon EMR, and selecting the appropriate tools based on factors like data structure, access patterns, and data temperature. It also discusses stream/real-time analytics tools and machine learning approaches.
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.
Cloud Operating Models for Accelerated Cloud Transformation - AWS Summit SydneyAmazon Web Services
In this session you will learn about how the adoption of a Cloud Operating Model can accelerate your cloud transformation. You will learn about some of the effective Cloud Operating models (centralised, decentralised and distributed), how they affect organisational structures, and the role they play in digital transformation. Learn the role training plays in your accelerated transformation. Learn how to build and bootstrap a Cloud Center of Excellence (CCOE) and how they evolve as you transform your business in the adoption of cloud.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This presentation will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
Make your solution see, hear and talk, leveraging artificial intelligence services based on deep learning and neural networks. We will discover three new AI tools from AWS - Lex, Polly and Rekognition; integrated with AWS IoT and a physical world device for human interaction and environmental awareness.
In this presentation, we provide an overview of Cloud Computing and provide some details on the wide range of services that Amazon Web Services offers today. This presentation is intended for people new to cloud computing or experienced cloud developers who have not yet used AWS.
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon Machine Learning (Amazon ML) services work together to build a successful data lake for various roles, including data scientists and business users.
The document provides an overview of Amazon's machine learning capabilities including:
- Platform services like EC2 P3 instances and Deep Learning AMIs for training models
- Managed services like SageMaker for building, training, and deploying models, and applications services like Rekognition, Transcribe, Translate, and Comprehend for vision, speech and text analysis
- It describes how these capabilities are used across Amazon for applications like fulfilment, search, and developing new products
The document provides an overview of Amazon Web Services (AWS) including its global infrastructure, key services, and security practices. It discusses AWS' 13+ years of experience and 165 cloud services. Specific AWS services covered include compute, storage, databases, security, and containers. Pricing and availability of AWS services are also summarized.
Learn about the new AWS Database Migration Service, which helps you migrate databases with minimal downtime from on-premises and Amazon EC2 environments to Amazon RDS, Amazon Redshift, Amazon Aurora and EC2 databases. We discuss homogeneous (e.g. Oracle-to-Oracle, PostgreSQL-to-PostgreSQL, etc.) and heterogeneous (e.g. Oracle to Aurora, SQL Server to MariaDB) database migrations. We also talk about the new AWS Schema Conversion Tool that saves you development time when migrating your Oracle and SQL Server database schemas, including PL/SQL and T-SQL procedural code, to their MySQL, MariaDB and Aurora equivalents.
AWS Webinar Series - Cost Optimisation Levers, Tools, and StrategiesAmazon Web Services
The document discusses strategies for optimizing costs when using AWS. It covers establishing cost visibility using tools like AWS Cost Explorer. It also discusses technical optimization levers like right-sizing resources, using reserved instances, increasing infrastructure elasticity, matching storage classes to needs, and designing architectures for lower costs. The presentation provides examples and recommendations for how to apply these optimization strategies on AWS.
Today organizations find themselves in a data rich world with a growing need for increased agility and accessibility of all this data for analysis and deriving keen insights to drive strategic decisions. Creating a data lake helps you to manage all the disparate sources of data you are collecting (in its original format) and extract value. In this session, learn how to architect and implement a data lake in the AWS Cloud. Learn about best practices as we walk through architectural blueprints.
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.
This document discusses implementing a data lake on AWS to securely store, categorize, and analyze all types of data in a centralized repository. It describes key attributes of a data lake like decoupled storage and compute, rapid ingestion and transformation, and schema on read. It then outlines various AWS services that can be used to build a data lake like S3, Athena, EMR, Redshift, Glue, and Kinesis. It provides examples of streaming IoT data into a data lake and running queries and analytics on the data.
Introduction to the Well-Architected Framework and Tool - SVC212 - Chicago AW...Amazon Web Services
Most modern businesses depend on a portfolio of technology solutions to successfully operate every day. How do you know whether your team is following best practices or what the risks are in your architectures? In this session, we show how the AWS Well-Architected Framework provides prescriptive advice on best practices as well as 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.
Most modern businesses depend on a portfolio of technology solutions to successfully operate every day. How do you know whether your team is following best practices or what the risks are in your architectures? In this session, we show how the AWS Well-Architected Framework provides prescriptive advice on best practices as well as 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.
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.
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.
The document describes a presentation on Amazon Athena, a serverless interactive query service that allows users to analyze data directly from Amazon S3 using standard SQL. The presentation will introduce Athena and demonstrate how it can be used to query data in S3 without having to load it into a database first. It will also discuss how Athena uses Presto and the Glue Data Catalog under the hood and show some customer use cases for log analysis, ETL workflows, and analytics reporting using Athena with other AWS services.
Data pipeline and data lake for autonomous drivingYu Huang
This document outlines autonomous driving data pipelines and data lakes used by various companies. It discusses Tesla, Google Waymo, PlusAI, Alibaba Cloud, Nvidia, NetApp, Amazon AWS, Amazon TRI, Amazon Momenta, and data pipeline strategies from Eckerson DataOps and IBM. The document also provides a detailed overview of an autonomous driving data lake built on AWS that ingests vehicle telemetry data and processes drive data for labeling and search capabilities.
The document discusses Amazon's perspective on the next wave of retailing. It highlights Amazon's scale, speed, innovation, and customer obsession. It discusses how Amazon focuses on customer experience, growth, traffic, selection, lower prices and lower costs. It also discusses challenges retailers face with technology and the opportunities for digital transformation.
The document discusses Amazon Go, a cashier-less convenience store developed by Amazon, as a case study for using machine learning and IoT on AWS. It provides an overview of the challenges in building Amazon Go, including identifying customers and items taken for purchase without traditional checkout methods. It then discusses retail trends driving the need for AI and IoT solutions. The remainder of the document offers tips for developing AI and IoT projects, including focusing on business outcomes, considering related AWS services, and learning through hands-on projects to avoid prolonged analysis.
Unlock the Full Potential of Your Media Assets, ft. Fox Entertainment Group (...Amazon Web Services
The document discusses Amazon Rekognition and how it can be used by media companies like Fox Entertainment Group to unlock the full potential of their media assets. It describes Amazon Rekognition's capabilities for image and video analysis like facial recognition. It also provides examples of how companies can use Amazon Rekognition for media discovery, content moderation, and generating automated metadata to power new workflows and applications.
Amazon reInvent 2020 Recap: AI and Machine LearningChris Fregly
Amazon reInvent 2020 Recap: AI and Machine Learning
Video here: https://youtu.be/YSXe02Y5pHM
NEW RELEASE! Build, Automate, Manage, and Scale ML Workflows with the NEW Amazon SageMaker Pipelines by Hallie Crosby Weishahn.
Description of Talk and Demo
AWS recently announced Amazon SageMaker Pipelines (https://aws.amazon.com/sagemaker/pipelines/), the first purpose-built, easy-to-use Continuous Integration and Continuous Delivery (CI/CD) service for machine learning.
SageMaker Pipelines has three main components which improve the operational resilience and reproducibility of your workflows: 1) pipelines, 2) model registry, and 3) projects.
In this talk and demo, Hallie will walk us through the new Amazon SageMaker Pipelines feature including MLOps support.
Date/Time
9-10am US Pacific Time (Third Monday of Every Month)
RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154
Meetup:
https://www.meetup.com/Data-Science-on-AWS/
Zoom:
https://zoom.us/j/690414331
Webinar ID: 690 414 331
Phone:
+1 646 558 8656 (US Toll) or +1 408 638 0968 (US Toll)
Related Links
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
O'Reilly Book: https://datascienceonaws.com
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com
Support: https://support.pipeline.ai
Monthly Workshop: https://www.eventbrite.com/e/full-day-workshop-kubeflow-gpu-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-tickets-63362929227
RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154
Deep Dive on Amazon Rekognition, ft. Pinterest (AIM307-R1) - AWS re:Invent 2018Amazon Web Services
Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications.
The document discusses Amazon Web Services (AWS) Machine Learning capabilities. It describes the AWS ML stack, which includes AI and ML services, frameworks and infrastructure like Amazon SageMaker. SageMaker provides tools to help with tasks like data processing, model training, deployment and monitoring. It also addresses common challenges in ML like skills gaps and complex model building processes. Customers can leverage AWS ML services and tools to build innovative solutions across domains like computer vision, natural language processing and forecasting.
The document summarizes sessions from the AWS re:Invent 2018 conference related to digital advertising and machine learning. It provides an index of breakout sessions, chalk talks, and workshops on topics like using Amazon Rekognition for video analysis, global cloud architecture, and real-time targeted advertising. A highlighted session discusses how The Trade Desk has evolved to use AI/ML techniques and another session describes how VidMob leverages Amazon Rekognition for video creative asset production and analysis.
Create an ML Factory in Financial Services with CI CD - FSI301 - New York AWS...Amazon Web Services
The document discusses creating a machine learning factory using AWS services. It describes combining Amazon SageMaker (for building, training, and deploying ML models) with Amazon CodeCommit, CodeBuild, and CodePipeline to create an automated pipeline. When model code or training data changes are committed to CodeCommit, CodePipeline will trigger CodeBuild to build a Docker image, train a model in SageMaker, and deploy the new model. This allows for continuous integration and deployment of ML models, improving the development process for highly-regulated industries like financial services.
Deep Dive on Amazon Rekognition, ft. Tinder & News UK (AIM307-R) - AWS re:Inv...Amazon Web Services
Join us for a deep dive on the latest features of Amazon Rekognition. Learn how to easily add intelligent image and video analysis to applications in order to automate manual workflows, enhance creativity, and provide more personalized customer experiences. We share best practices for fine-tuning and optimizing Amazon Rekognition for a variety of use cases, including moderating content, creating searchable content libraries, and integrating secondary authentication into existing applications.
How Avatars & AR Are Driving Innovation: Lessons from Electronic Caregiver (A...Amazon Web Services
Electronic Caregiver has embraced the use of augmented reality, artificial intelligence, machine learning, and virtual avatars to transform how it provides health and aging services. In this session, learn how the company translated 20 years of research and clinical experience into an innovative new product line, Addison Care, built on AWS services. We provide an overview of the architecture and facilitate a discussion on selecting and integrating various services. Come prepared for a lively session.
Going from a hypothesis to a working machine learning model that infers answers in production requires a lot of time and effort. Moreover, the ability to answer questions related to specific results—such as, “what version of the code and data produced a particular inference?”—is paramount in highly regulated industries such as Financial Services. Modern development practices like continuous integration and deployment can accelerate the machine learning development process and provide a way to answer questions about data lineage. During this talk, you will learn how to combine Amazon SageMaker (a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale) with Amazon CodeCommit, CodeBuild, and CodePipeline to create a pipeline that automatically triggers changes when either your model code or training data changes.
Presenter: Felix Candelario, Principal Global Account Solutions Architect, AWS
Amazon SageMaker Clarify (https://aws.amazon.com/sagemaker/clarify/) provides machine learning developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions. SageMaker Clarify detects potential bias during data preparation, after model training, and in your deployed model by examining attributes you specify. For instance, you can check for bias related to age in your initial dataset or in your trained model and receive a detailed report that quantifies different types of possible bias. SageMaker Clarify also includes feature importance graphs that help you explain model predictions and produces reports which can be used to support internal presentations or to identify issues with your model that you can take steps to correct.
For more information on Amazon SageMaker Clarify, please refer these links: (1) https://aws.amazon.com/sagemaker/clarify (2) https://aws.amazon.com/blogs/aws/new-amazon-sagemaker-clarify-detects-bias-and-increases-the-transparency-of-machine-learning-models (3) https://github.com/aws/amazon-sagemaker-clarify (4) Discussion and demo: https://youtu.be/cQo2ew0DQw0
Acknowledgments: Amazon SageMaker Clarify core team, Amazon AWS AI team, and partners across Amazon
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.
This document discusses building a retail data platform using AI and machine learning. It describes ingesting various data types, storing the data in data lakes, processing and analyzing the data. Machine learning models can then be developed and deployed to drive business outcomes like increased revenue, customer satisfaction and supply chain efficiency. Case studies are presented of companies like Tapestry that have built such platforms on AWS to enable data-driven decision making, predictive analytics and continuous improvement using techniques like automatic model retraining.
Amazon Rekognition: Deep Learning-Based Image and Video Analysis - BDA303 - C...Amazon Web Services
This document discusses Amazon Rekognition, Amazon's deep learning-based image and video analysis service. It provides an overview of Amazon Rekognition's capabilities for image and video analysis, including object and scene detection, facial analysis, face recognition, text detection in images, and more. It also discusses examples of customers like VidMob that use Amazon Rekognition for tasks like analyzing video content, moderating user-generated content, and powering face-based features.
Amazon QuickSight è un servizio di business intelligence veloce e innovativo che consente di fornire informazioni dettagliate a tutti gli utenti dell'organizzazione. Come servizio completamente gestito, QuickSight consente di creare e pubblicare facilmente dashboard interattive che includono funzionalità uniche quali ML Insights, Ml Powered Forecasts and Anomaly Detection. Le dashboard sono quindi accessibili da qualsiasi dispositivo e possono essere integrate in applicazioni, portali e siti Web. Nell'ultimo anno QuickSight ha rilasciato oltre 200 nuove funzionalità. In questo webinar forniamo una panoramica dettagliata di QuickSight e una demo live per apprezzarne appieno il potenziale.
Artificial intelligence in actions: delivering a new experience to Formula 1 ...GoDataDriven
At GoDataFest 2019, Guy Kfir presented how AI delivers a new experience to Formula 1 fans across the world. AWS fuels the analytics through machine learning. Did you know a Formula 1 race car contains 120 sensors and generated 3 GB of data every race at 1,500 data points per second? AWS developed several applications, including overtake possibility, pitstop advantage. How important is it for your company to invest in Machine Learning and AI? There are three scenario's for AI/ML success: Automation, Enrichment and Invention. So, what are you waiting for: create the loop, advance your data strategy and organize for succes. To get started identify AI/ML use cases, educate yourself, start with AI services and move to Amazon Sagemaker, engage with AWS, consider the partner eco system (like GoDataDriven or Binx).
Real-World AI and Deep Learning for Enterprise with Case StudiesAmazon Web Services
Artificial Intelligence is here this time, to stay. For the Enterprise, AI materializes into solutions that improve customers' experiences by optimizing, automating, and personalizing high-volume tasks while lowering cost and time to market, therefore accelerating innovation. In this session, we cover AWS' AI products and services that enable innovation in the enterprise while maintaining compliance with different regimes such as HIPAA, PCI, and more. Finally, we discuss enterprise architectures on AWS for machine learning and deep learning workloads.
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.
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.
Durante i laboratori pratici, gli esperti AWS ti mostrano quali strumenti aiutano a sviluppare le applicazioni Serverless in locale e nel cloud AWS e ti aiuteranno a programmare i prossimi passi per iniziare ad utilizzare questa tecnologia nella tua azienda.
AWS Serverless per startup: come innovare senza preoccuparsi dei serverAmazon Web Services
Serverless computing allows developers to build and run applications without having to manage infrastructure. With serverless, applications can automatically scale as usage increases and developers only pay for the resources consumed. Serverless services on AWS include AWS Lambda, API Gateway, DynamoDB, S3 and more which can be combined into serverless applications and architectures. AWS also provides training and certifications to help developers learn serverless concepts and services.