Machine Learning Week at the San Francisco Loft: Build Computer Vision Applications with Amazon Rekognition
Build Computer Vision Applications with Amazon Rekognition
This hands-on workshop will walk through how to build a solution that listens and captures images from Twitter, and then compares those images against a reference image to automatically notify you about a new post featuring your favorite celebrity. Additionally, we will integrate sentiment analysis into this image-based automatic alert system in order to gauge whether the determined celebrities are happy, sad, etc. in the posted image.
Level: 200-300
Speaker: Niranjan Hira - Solutions Architect, Amazon Lex
Build Computer Vision Applications with Amazon RekognitionAmazon Web Services
Amazon Rekognition is a deep learning-based image and video analysis service that enables developers to integrate easy-to-use APIs into their applications. This session will walk through the Amazon Rekognition features, including object and scene detection, text-in-image extraction, celebrity recognition, content moderation, and more. Developers can quickly get started with this fully-managed service and start building computer vision applications such as a searchable media library, automated content moderation, an image-based alert system, and more.
Level: 200-300
Speaker: Binoy Das - Partner Solutions Architect, AWS
Build Computer Vision Applications with Amazon RekognitionAmazon Web Services
Amazon Rekognition is a deep learning-based image and video analysis service that enables developers to integrate easy-to-use APIs into their applications. This session will walk through the Amazon Rekognition features, including object and scene detection, text-in-image extraction, celebrity recognition, content moderation, and more. Developers can quickly get started with this fully-managed service and start building computer vision applications such as a searchable media library, automated content moderation, an image-based alert system, and more.
Level: 200-300
Speaker: Liam Morrison - Principal Solutions Architect, AWS
Build Computer Vision Applications with Amazon RekognitionAmazon Web Services
Amazon Rekognition is a deep learning-based image and video analysis service that enables developers to integrate easy-to-use APIs into their applications. This session will walk through the Amazon Rekognition features, including object and scene detection, text-in-image extraction, celebrity recognition, content moderation, and more. Developers can quickly get started with this fully-managed service and start building computer vision applications such as a searchable media library, automated content moderation, an image-based alert system, and more.
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.
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.
When people refer to “organic SEO” (search engine optimization), they almost always use it as a blanket term to describe the unpaid, algorithm-driven results of any particular engine. However, a sophisticated search engine optimization company will often take the meaning of “organic” one step further. To such companies, the description of “organic SEO” is not limited to what shows up in the “natural” search engine results – it includes the methodologies used to achieve such rankings.
SEO – Search Engine Optimization, breaks in two parts first we will understand about the Search Engines, a platform we will be using to rank our website #1, later we will understand the optimization part. At the end there’s a bonus part which will help you track and analyze the work you we will be undertaking with this guided process.
BDA303 Amazon Rekognition: Deep Learning-Based Image and Video AnalysisAmazon Web Services
Learn how Amazon Rekognition is using deep learning-based image and video analysis to power more targeted influencer marketing and advertising, analysis of user-generated content on social platforms, real-time public safety alerts, and visual authentication in banking applications. In this session, we provide an overview of Amazon Rekognition image and video features, highlight customer stories from specific vertical use cases, such as influencer marketing, media, and public safety, and walk through some demonstrations and architectures for common use cases.
Build Computer Vision Applications with Amazon RekognitionAmazon Web Services
Amazon Rekognition is a deep learning-based image and video analysis service that enables developers to integrate easy-to-use APIs into their applications. This session will walk through the Amazon Rekognition features, including object and scene detection, text-in-image extraction, celebrity recognition, content moderation, and more. Developers can quickly get started with this fully-managed service and start building computer vision applications such as a searchable media library, automated content moderation, an image-based alert system, and more.
Level: 200-300
Speaker: Binoy Das - Partner Solutions Architect, AWS
Build Computer Vision Applications with Amazon RekognitionAmazon Web Services
Amazon Rekognition is a deep learning-based image and video analysis service that enables developers to integrate easy-to-use APIs into their applications. This session will walk through the Amazon Rekognition features, including object and scene detection, text-in-image extraction, celebrity recognition, content moderation, and more. Developers can quickly get started with this fully-managed service and start building computer vision applications such as a searchable media library, automated content moderation, an image-based alert system, and more.
Level: 200-300
Speaker: Liam Morrison - Principal Solutions Architect, AWS
Build Computer Vision Applications with Amazon RekognitionAmazon Web Services
Amazon Rekognition is a deep learning-based image and video analysis service that enables developers to integrate easy-to-use APIs into their applications. This session will walk through the Amazon Rekognition features, including object and scene detection, text-in-image extraction, celebrity recognition, content moderation, and more. Developers can quickly get started with this fully-managed service and start building computer vision applications such as a searchable media library, automated content moderation, an image-based alert system, and more.
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.
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.
When people refer to “organic SEO” (search engine optimization), they almost always use it as a blanket term to describe the unpaid, algorithm-driven results of any particular engine. However, a sophisticated search engine optimization company will often take the meaning of “organic” one step further. To such companies, the description of “organic SEO” is not limited to what shows up in the “natural” search engine results – it includes the methodologies used to achieve such rankings.
SEO – Search Engine Optimization, breaks in two parts first we will understand about the Search Engines, a platform we will be using to rank our website #1, later we will understand the optimization part. At the end there’s a bonus part which will help you track and analyze the work you we will be undertaking with this guided process.
BDA303 Amazon Rekognition: Deep Learning-Based Image and Video AnalysisAmazon Web Services
Learn how Amazon Rekognition is using deep learning-based image and video analysis to power more targeted influencer marketing and advertising, analysis of user-generated content on social platforms, real-time public safety alerts, and visual authentication in banking applications. In this session, we provide an overview of Amazon Rekognition image and video features, highlight customer stories from specific vertical use cases, such as influencer marketing, media, and public safety, and walk through some demonstrations and architectures for common use cases.
BDA304 Build Deep Learning Applications with TensorFlow and Amazon SageMakerAmazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. In this session, you learn how to get started with the TensorFlow deep learning framework using Amazon SageMaker, a platform to easily build, train, and use to deploy models at scale. You learn how to build a model using TensorFlow by setting up a Jupyter Notebook to get started with image and object recognition. You also learn how to quickly train and deploy a model through Amazon SageMaker.
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.
Amazon Rekognition: Deep Learning-Based Image and Video AnalysisAmazon Web Services
Learn how Amazon Rekognition is using deep learning-based image and video analysis to power data-driven decision making for creative asset production, more targeted influencer marketing and advertising, and visual authentication for a variety of use cases. Learn about Amazon Rekognition image and video features. Hear customer stories from specific vertical use cases, such as media and advertising, and walk through some demonstrations and architectures for common use cases.
Enriching your app with Image recognition and AWS AI services Hebrew WebinarBoaz Ziniman
Artificial Intelligence services on the AWS cloud bring machine learning technologies such as image recognition and computer vision within reach of every developer.In this session, you will be introduced to AWS AI services for developers and learn how to use one of them, Amazon Rekognition, to add new capabilities to your applications.
Use Amazon Rekognition to Power Video Creative Asset Production (ADT202) - AW...Amazon Web Services
The document discusses how VidMob, a creative technology platform, uses Amazon Rekognition to analyze video creative assets. Amazon Rekognition allows VidMob to automatically extract metadata like objects, scenes, faces, and text from videos. This creative data provides insights into how different elements of videos influence viewer engagement. VidMob's platform then helps brands optimize their video creative based on these insights to improve marketing outcomes.
The document discusses video analysis using Amazon Rekognition and Amazon Kinesis Video Streams. It provides an overview of Amazon's AI capabilities and services, including Amazon Rekognition for image and video analysis. Amazon Rekognition Video allows analyzing stored or live streaming video for tasks like object detection, facial recognition and celebrity recognition. Amazon Kinesis Video Streams can be used to stream video data to applications. Examples of applications discussed include public safety, media/entertainment, home monitoring, and manufacturing.
The document outlines an agenda for a day-long event on AI and machine learning. It begins with an introductory session on the state of AI from 10:00-11:00 am. This is followed by a break and then deeper sessions on Amazon Sagemaker, Forecast, and Personalize. Lunch is from 12:30-1:30 pm. The afternoon includes sessions on machine learning production with Sagemaker and fraud detection with Sagemaker. There are additional breaks throughout the day and the event concludes with a session on reinforcement learning from 3:45-4:45 pm.
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.
Join us to learn why Human-in-the-Loop training data should be powering your machine learning (ML) projects and how to make it happen. If you’re curious about what human-in-the-loop machine learning actually looks like, join Figure Eight CTO Robert Munro and AWS machine learning experts to learn how to effectively incorporate active learning and human-in-the-loop practices in your ML projects to achieve better results.
You'll learn:
- When to use human-in-the-loop as an effective strategy for machine learning projects
- How to set up an effective interface to get the most out of human intelligence
- How to ensure high-quality, accurate data sets
Artificial Intelligence nella realtà di oggi: come utilizzarla al meglioAmazon Web Services
L'intelligenza Artificiale è qui questa volta, per restare. Per le aziende, l'intelligenza artificiale si concretizza in soluzioni che migliorano l'esperienza dei clienti ottimizzando, automatizzando e personalizzando attività ad alto volume e riducendo al contempo costi e tempi, accelerando notevolmente il ritmo di innovazione. In questa sessione, approfondiremo i servizi AI di AWS che promuovo l'innovazione in azienda mantenendo la conformità con diversi regimi come HIPAA, PCI e altro. Infine, presenteremo le architetture AWS necessarie per supportare i carichi di lavoro di apprendimento automatico e deep learning.
The document discusses Amazon's AI services for building machine learning models including application services, platform services, and frameworks/infrastructure. It describes several Amazon AI services such as Amazon Rekognition for computer vision, Amazon Polly for text-to-speech, Amazon Lex for conversational interfaces, and Amazon SageMaker for training and deploying models. The services provide APIs, tools, and capabilities to developers and data scientists to incorporate AI into their applications and analyze large datasets.
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.
The document discusses Amazon Web Services' (AWS) machine learning and artificial intelligence services. It provides an overview of AWS' application services like Amazon Rekognition, Amazon Polly, and Amazon Translate. It also discusses AWS' platform services like Amazon SageMaker, Amazon EMR, and the AWS Deep Learning AMI. The document emphasizes that more AI/ML is built on AWS than anywhere else and highlights several customer examples using AWS machine learning services.
Machine learning state of the union - Tel Aviv Summit 2018Amazon Web Services
Join us to hear about our strategy for driving machine learning innovation for our customers and learn what's new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe and Amazon Comprehend.
Attend this session to understand how to make the most of machine learning in the cloud.
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.
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.
Build, Deploy, and Serve Machine Learning Models on Streaming Data (ANT345-R1...Amazon Web Services
As data grows exponentially in organizations, there is an increasing need to use machine learning (ML) to gather insights from this data at scale and use those insights for real-time predictions on incoming data. This workshop walks you through the following: 1) training a Spark Model using Amazon SageMaker pointed to Apache Livy running on an Amazon EMR Spark cluster, 2) hosting the Spark model on Amazon SageMaker to serve a RESTful inference API, 3) and using the RESTful API to serve real-time predictions on streaming data from Amazon Kinesis Data Streams.
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.
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Learn how Amazon Rekognition is using deep learning-based image and video analysis to power data-driven decision making for creative asset production, more targeted influencer marketing and advertising, and visual authentication for a variety of use cases. Learn about Amazon Rekognition image and video features. Hear customer stories from specific vertical use cases, such as media and advertising, and walk through some demonstrations and architectures for common use cases.
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Artificial Intelligence services on the AWS cloud bring machine learning technologies such as image recognition and computer vision within reach of every developer.In this session, you will be introduced to AWS AI services for developers and learn how to use one of them, Amazon Rekognition, to add new capabilities to your applications.
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The document discusses how VidMob, a creative technology platform, uses Amazon Rekognition to analyze video creative assets. Amazon Rekognition allows VidMob to automatically extract metadata like objects, scenes, faces, and text from videos. This creative data provides insights into how different elements of videos influence viewer engagement. VidMob's platform then helps brands optimize their video creative based on these insights to improve marketing outcomes.
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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.
Join us to learn why Human-in-the-Loop training data should be powering your machine learning (ML) projects and how to make it happen. If you’re curious about what human-in-the-loop machine learning actually looks like, join Figure Eight CTO Robert Munro and AWS machine learning experts to learn how to effectively incorporate active learning and human-in-the-loop practices in your ML projects to achieve better results.
You'll learn:
- When to use human-in-the-loop as an effective strategy for machine learning projects
- How to set up an effective interface to get the most out of human intelligence
- How to ensure high-quality, accurate data sets
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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.
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Attend this session to understand how to make the most of machine learning in the cloud.
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Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
1) The document discusses building a minimum viable product (MVP) using Amazon Web Services (AWS).
2) It provides an example of an MVP for an omni-channel messenger platform that was built from 2017 to connect ecommerce stores to customers via web chat, Facebook Messenger, WhatsApp, and other channels.
3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.
This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.
This document discusses building serverless web applications using AWS services like API Gateway, Lambda, DynamoDB, S3 and Amplify. It provides an overview of each service and how they can work together to create a scalable, secure and cost-effective serverless application stack without having to manage servers or infrastructure. Key services covered include API Gateway for hosting APIs, Lambda for backend logic, DynamoDB for database needs, S3 for static content, and Amplify for frontend hosting and continuous deployment.
This document provides tips for fundraising from startup founders Roland Yau and Sze Lok Chan. It discusses generating competition to create urgency for investors, fundraising in parallel rather than sequentially, having a clear fundraising narrative focused on what you do and why it's compelling, and prioritizing relationships with people over firms. It also notes how the pandemic has changed fundraising, with examples of deals done virtually during this time. The tips emphasize being fully prepared before fundraising and cultivating connections with investors in advance.
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.