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.
Customers who run SAP on AWS have lowered costs, improved performance, resilience, security, and agility. Application modernization can start with SAP at the core – but it can also start with machine learning, internet of things, big data and analytics. In this session, AWS is presenting and demonstrating use cases for modernizing IT systems that incorporates SAP. Customer Larsen & Toubro Infotech (LTI) shares their innovation agenda and journey to the cloud with AWS.
Harpreet Singh, SAP Solution Architect, Amazon Web Services
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.
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!
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.
Modul ini membahas beberapa layanan untuk mendukung Anda dalam membangun di cloud dari aplikasi yang mudah diskalakan, pemantauan sumber daya Anda, automasi penerapan, koneksi dan berbagi data, serta penyampaian konten.
AWS Business Essentials helps IT business leaders and professionals understand the benefits of cloud computing and how a cloud strategy can help you meet your business objectives. In this course we discuss the advantages of cloud computing for your business and the fundamentals of AWS, including financial benefits. This course also introduces you to successful cloud adoption frameworks so to help you consider the AWS platform within your cloud computing strategy. We have broken this training into 3 parts during the event, in order to complete the training please plan to attend all 3 sessions.
This document discusses strategies for implementing multi-account architectures on AWS. It recommends creating separate AWS accounts for different purposes such as development, testing, production, logging, security tools, and shared services. It also recommends using AWS Organizations to centrally manage these accounts and AWS Control Tower to automate the setup and governance of multi-account environments according to best practices. AWS Control Tower provides features like pre-configured guardrails, identity management with AWS SSO, log aggregation, and self-service provisioning to help customers manage security, compliance and operations at scale across multiple AWS accounts.
Journey to the cloud: a cosa deve pensare un’organizzazione che vuole migrare...Amazon Web Services
La complessità di un programma di cloud transformation che coinvolge la migrazione di centinaia o addirittura migliaia di server può essere una sfida importante per il program management e per il coordinamento dell’IT team incaricato del successo e del supporto di queste attività di preparazione e di migrazione. Questa sessione mette in luce il framework di AWS, altamente ripetibile e scalabile e il metodo che sta aiutando i clienti ad essere pronti per un’esecuzione accelerata della loro migrazione o la preparazione delle operazioni per i workload in esercizio da portare su AWS.
The document provides best practices for migrating enterprise workloads to AWS. It discusses common business drivers for cloud migration like agility, cost reduction, and digital transformation. Case studies are presented showing organizations achieving benefits like cost savings, improved productivity, and reduced risk by migrating applications and infrastructure to AWS. The migration process involves assessment, planning, and executing the migration. Various migration strategies and tools are outlined to help simplify and accelerate migrating workloads to AWS.
There are legacy enterprise Microsoft applications still running on premises, Microsoft SharePoint, Dynamics, Exchange, SQL server or .NET applications. To best realize the benefits of cloud, these applications must be modernized using cloud native approaches to become scalable, secure and fault tolerant. The webinar covers how to refactor and modernize Microsoft applications, explore methods to integrate with AWS managed services for identity federation, databases, monitoring and containers to achieve agility, security and elasticity.
Sriwantha Attanayake, Partner Solution Architect, Amazon Web Services
Training Overview:
This free, online training will provide an introduction to the core AWS services for compute, storage, database, and networking. Our AWS technical expert will provide an overview of AWS, sharing key features, use cases, best practices, and walk through technical demos. There will be AWS experts available to answer your questions one-on-one.
Who should attend:
Virtual AWSome Day is ideal for IT managers, system engineers, system administrators, and architects who are eager to learn more about AWS cloud computing.
AWS Introduction & History - AWSome Day Philadelphia 2019Amazon Web Services
Part 1 of 3.
This free, one-day training will provide a step-by-step introduction to the core AWS services for compute, storage, database, and networking.
AWS technical experts will explain key features and use cases, share best practices, walk through technical demos, and be available to answer your questions one-on-one.
Who should attend?
AWSome Day is ideal for IT managers, system engineers, system administrators, and architects who are eager to learn more about cloud computing and how to get started on the AWS Cloud.
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.
Accelerate and Modernise Microsoft Workload Migrations on AWSAmazon Web Services
This document discusses accelerating and modernizing Microsoft workload migrations to AWS. It provides an overview of Microsoft workloads that can run on AWS, optimization levers, and architecture options for licensing and resource consumption. Best practices for migration acceleration and modernization are presented, along with a case study of Guinness World Records' experience migrating to AWS. Resources and programs for getting started with Windows workload migrations on AWS are also outlined.
1) AWS Outposts allow customers to run compute and storage on-premises using the same AWS infrastructure, APIs, and tools that are used in AWS regions.
2) Outposts are rack-sized physical infrastructure deployed on the customer's premises that is managed and operated by AWS.
3) Customers can launch and run EC2 instances, EBS volumes, and other AWS services locally on Outposts to process workloads requiring low latency or local data access.
This document summarizes a presentation on linear streaming technology innovations from the Japanese television industry. It discusses the challenges of migrating television services to over-the-top (OTT) delivery, including integrating with on-premise encoders, meeting strict alert times, and providing frame-accurate catch-up TV within 15 minutes. It describes the solutions from KKStream using AWS infrastructure like DirectConnect, MediaConnect, MediaLive and MediaPackage to securely ingest live streams, switch between redundant streams, and harvest and trim live content to enable frame-accurate catch-up TV without manual effort. Monitoring metrics ensure stability and alerts are sent if thresholds are exceeded. Migrating television to the cloud can help reach larger audiences
This document provides an overview of Amazon Web Services (AWS) including its history, services, pricing model, global infrastructure, and how customers can get started with AWS. It describes how AWS began as Amazon's internal infrastructure and has grown to serve over 1 million customers globally across industries like startups, enterprises, and government agencies. The document outlines AWS's broad range of cloud computing services across categories like compute, storage, databases, analytics, mobile, and more. It emphasizes AWS's focus on innovation with new services and features, lower prices through economies of scale, and its utility-based on-demand pricing model. Finally, it suggests steps for getting started like using the free tier, training, and certification programs.
This document discusses using AWS services like AWS RoboMaker to simulate, test, and deploy robotics applications at scale. It describes how RoboMaker allows simulating robots and testing applications through cloud-based simulation. This enables large-scale regression testing with CI/CD integration and multi-robot simulations. RoboMaker also facilitates machine learning model training using simulation data. Finally, it discusses how RoboMaker allows deploying applications to robot fleets and managing updates through its cloud-scale fleet management capabilities.
Speech deliverd on 20 June 2020 at TR.AI Meetup, Istanbul
TR.AI Türkiye Yapay Zeka İnisiyatifi
AI/ML PoweredPersonalized Recommendations in Gaming Industry
Amazon Web Services - AWS
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
The document discusses Amazon SageMaker, a fully managed service that allows users to build, train, and deploy machine learning models at scale. It provides an overview of SageMaker's key features like notebooks for preprocessing data and building models, built-in algorithms for common tasks, one-click training of models, hyperparameter tuning, and deployment of trained models onto managed hosting infrastructure. SageMaker aims to make machine learning accessible to every developer by handling the complexities of training and deploying models.
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.
This session highlights how Earth observation data shared in the cloud is accelerating research in machine learning that can have a dramatic impact on the effectiveness of future warfighting capability. Come learn about SpaceNet, a project sponsored by CosmiQ Works, DigitalGlobe, and Nvidia that makes commercial satellite imagery available for machine learning research on AWS. In this session, you will learn how AWS machine learning services like SageMaker, hyper-scale GPU compute capacity, and datasets shared in the cloud can ultimately produce machine learning models that could have a dramatic impact on the effectiveness of future warfighting capability. As the DoD strives to bring innovation directly to the warfighter, a combination of global open data sets coupled with easily built ML models can give warfighters access to critical information when the mission needs it most.
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.
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.
Best practices for integrating Amazon Rekognition into your own applicationAmazon Web Services
The document discusses best practices for integrating Amazon Rekognition machine learning services into applications. It provides an overview of Rekognition capabilities like facial analysis, face detection and comparison. It also covers examples of optimizing input data, building searchable image libraries, sentiment analysis and face-based user verification using Rekognition with other AWS services.
WhereML a Serverless ML Powered Location Guessing Twitter BotRandall Hunt
Learn how we designed, built, and deployed the @WhereML Twitter bot that can identify where in the world a picture was taken using only the pixels in the image. We'll dive deep on artificial intelligence and deep learning with the MXNet framework and also talk about working with the Twitter Account Activity API. The bot is entirely autoscaling and powered by Amazon API Gateway and AWS Lambda which means, as a customer, you don't manage any infrastructure. Finally we'll close with a discussion around custom authorizers in API Gateway and when to use them.
Get Started with Deep Learning and Computer Vision Using AWS DeepLens (AIM316...Amazon Web Services
If you're new to deep learning, this workshop is for you. Learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Also learn to build a machine learning application and a model from scratch using Amazon SageMaker. Finally, learn to extend that model to Amazon SageMaker to build an end-to-end AI application.
The document discusses Amazon SageMaker, a fully managed machine learning platform from AWS. It provides built-in algorithms, frameworks, and tools for training and deploying machine learning models. SageMaker handles setting up environments, running training jobs, performing hyperparameter tuning, deploying models for inference, and managing and scaling the inference infrastructure. It aims to make machine learning accessible to every developer and data scientist.
Building the Organization of the Future: Leveraging AI & ML Amazon Web Services
Artificial intelligence and machine learning are no longer the stuff of science fiction. Organizations of all sizes are using these tools to create innovative artificial intelligence applications – namely, Amazon.com's own retail experience. Join us for an inside look at how Amazon thinks about this technology, and gain insight into a range of new machine learning services on AWS for use in your own organization.
Alex Coqueiro, Solutions Architect, Amazon Web Services
Manu Sud, Manager, Analytics and Advanced Technology Branch, Ontario Ministry of Economic Development, Job Creation and Trade
AI/ML with Data Lakes: Counterintuitive Consumer Insights in Retail (RET206) ...Amazon Web Services
In this session, learn how data scientists in the retail industry, from companies like Tapestry, Coach, and Kate Spade, are finding new, counterintuitive consumer insights using AWS artificial intelligence services in a data lake. By leveraging data from various retail systems, including CRM, marketing, e-commerce, point of sale, order management, merchandising, and customer care, we show you how these consumer insights might influence new and interesting retail use cases while establishing a data-driven culture within the organization. Services referenced include Amazon S3, Amazon Machine Learning, Amazon QuickSight, Amazon SageMaker, among others.
Supercharge GuardDuty with Partners: Threat Detection and Response at Scale (...Amazon Web Services
Amazon GuardDuty has the ability to detect threats. However, threat detection is only the first step. In this session, we combine the high fidelity findings of GuardDuty with partner products, and we demonstrate how to quickly respond, remediate, and prevent security incidents in order to supercharge and centralize your cloud security operations center (SOC).
The document discusses Amazon SageMaker, a fully managed machine learning platform that allows users to build, train, and deploy machine learning models at scale. It provides built-in algorithms, frameworks, and hosting to make machine learning more accessible. Key features include automatic model tuning, model compilation for deployment on various devices, and inference pipelines to preprocess and postprocess data for predictions. The document includes examples of using SageMaker for tasks like text classification and model tuning.
Julien Simon, Principal Technical Evangelist at Amazon - Machine Learning: Fr...Codiax
The document discusses Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models at scale. It provides pre-built algorithms, notebooks, and frameworks to simplify common ML tasks. Models can be trained using SageMaker's high-performance infrastructure and hyperparameter tuning capabilities. Trained models can then be deployed for prediction and scaled to production using SageMaker's hosting capabilities. The document highlights several SageMaker features including algorithms, compilation, inference pipelines, and customers.
Build a "Who's Who" App for Your Media Content (AIM409) - AWS re:Invent 2018Amazon Web Services
Video has become an increasingly successful medium for advertising, marketing, and engaging customers. However, many companies underutilize their substantial video assets because they are poorly indexed and cataloged. In this workshop, learn how to use machine learning services to gain more value from video by building a customer celebrity detection feature that can recognize mainstream celebrities and individuals from your own uploaded media files.
MongoDB .local London 2019: Using AWS to Transform Customer Data in MongoDB i...Lisa Roth, PMP
MongoDB is a popular database for many customer-centric use cases that include a single-view of the customer, and customer engagement systems involving personalization, catalogs and more. In this session we'll explore the possibilities of AI and MongoDB. How can we extract more value from your customer-centric data in MongoDB and the Atlas data lake using machine learning (ML) on AWS? Learn how to leverage AutoML to deliver state-of-the-art deep learning models without data science expertise to deliver recommendations, intelligent content targeting, forecasting and predictive customer insights. Enable your data scientists to integrate MongoDB, the data lake, and the data science eco-system to accelerate ML projects using AWS's ML services.
Similar to Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Learning di AWS (20)
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.
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.
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
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.
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.
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.
Costruisci modelli di Machine Learning con Amazon SageMaker AutopilotAmazon Web Services
Amazon SageMaker AutoPilot è una funzionalità di Amazon SageMaker che crea automaticamente il miglior modello di apprendimento automatico per il tuo set di dati. Con SageMaker Autopilot, si fornisce un set di dati tabellare e si seleziona la variabile target da prevedere, che può essere numerica o categorica. SageMaker Autopilot esplorerà automaticamente diverse soluzioni per trovare il modello migliore. È quindi possibile distribuire direttamente il modello in produzione con un solo clic o esplorare le soluzioni consigliate con Amazon SageMaker Studio per migliorare ulteriormente la qualità del modello. In questo webinar approfondiremo questa capacità, con dimostrazioni pratiche su come utilizzare il servizio.
Si stima che i clienti abbiamo in totale 256 EB di file shares in locale. La gestione di questi file systems è onerosa e comporta problematiche sia di budget (CAPEX) che di operation (gestione, scalabilità, data protection). Tipicamente gli apparati NAS locali devono essere sostituiti ogni 3-5 anni, obbligando i clienti a fare un capacity planning pluriennale e richiedendo un progetto a sè stante per la migrazione dati.
Il passaggio al cloud di AWS consente ai clienti di pagare esattamente la quantità di spazio di archiviazione di file di cui hanno bisogno ora, senza costi o vincoli iniziali e ridimensionare la capacità necessaria durante la crescita dei dati senza dover stimare in anticipo di quanto avranno bisogno. Sfruttando soluzioni di file completamente gestite come Amazon FSx per Windows File Server, FSx Backup, i clienti non devono più preoccuparsi del sovraccarico amministrativo di impostazione, protezione, manutenzione e backup della propria infrastruttura di file.
La recente apertura della regione italiana MXP apre a nuovi scenari di hybrid cloud per la parte filesystem/SMB share.
Protect your applications from DDoS/BOT & Advanced AttacksAmazon Web Services
This document discusses strategies for protecting applications from DDoS and bot attacks using AWS and F5 technologies. It outlines common external threats such as SQL injection and SYN floods. It then describes AWS services like Shield Standard, Shield Advanced, WAF, and Firewall Manager that provide detection, mitigation and protection capabilities. The benefits of these services include automatic protection, custom rule creation, access to response teams, and central management. It also outlines F5's managed security solutions for bot protection, threat intelligence and firewall management that are designed for multi-cloud environments.
This document discusses Amazon SageMaker, AWS's machine learning platform. It summarizes that SageMaker provides fully managed machine learning workflows including data processing, model training/tuning, deployment and monitoring. It aims to make machine learning accessible to every developer by automating workflows and providing pre-built algorithms, notebooks and debugging/optimization tools. The document highlights how SageMaker integrates with other AWS AI/ML services to provide a comprehensive platform for building machine learning solutions.
34. Auto Scaling group
Availability Zone 1
Availability Zone 2
Availability Zone 3
Deployment / Hosting
Amazon SageMaker ML
Compute Instances
SageMaker Endpoints
Elastic
Load Balancing
Model
Endpoint
Amazon
API
Gateway
Input Data
(Request)
Prediction
(Response)
Client
Model
saved in S3
Elastic
Container
Registry
Trained
model
Inference
container
41. Appendix – other useful links
https://sagemaker.readthedocs.io/en/stable/framework
s/pytorch/using_pytorch.html#train-a-model-with-
pytorch
Using PyTorch
script
documentation
Github Sagemaker
Examples
https://github.com/aws/amazon-sagemaker-examples
Github using
PyTorch script
example
https://github.com/aws/amazon-sagemaker-
examples/tree/master/sagemaker-python-
sdk/pytorch_cnn_cifar10
Note: there has been a recent update of the SageMaker SDK to version 2.0. Some examples are written for the previous SageMaker SDK. Please,
notice this link for further details:
https://sagemaker.readthedocs.io/en/stable/v2.html
You may downgrade temporarily with the following terminal command:
pip install sagemaker==1.72.0 –U
Upgrading to the latest SageMaker SDK can be done by executing:
pip install --upgrade sagemaker