The document discusses using AWS Step Functions to orchestrate workflows and serverless applications. It provides examples of how Step Functions can be used to coordinate various tasks in a photo sharing application, including extracting metadata from images, performing image recognition, generating thumbnails, and storing metadata. It outlines the different state types that can be used in Step Functions including tasks, choices, and parallel operations. The document also highlights how Step Functions allows sequencing functions, selecting functions based on data, retrying functions, and adding try/catch/catch functionality to workflows.
Getting Started with Combine And SwiftUIScott Gardner
In this 3 1/2-hour live hands-on workshop, you will learn the basics of SwiftUI Combine, and then create a multipeer chat app using SwiftUI and Combine.
Building Content Recommendation Systems using MXNet GluonApache MXNet
Netflix competition triggered a flurry of research for recommendation engines. This presentation provides a survey of techniques and models for creating a recommender system. The presentation covers Matrix Factorisation, Factorisation Machines, Distributed Factorisation Machines, and DSSM networks as well provide code examples for developing a Matrix Factorisation in Gluon. At the end the presentation provides tips and tricks for large-scale, realtime recommender engines.
Getting Started with Combine And SwiftUIScott Gardner
In this 3 1/2-hour live hands-on workshop, you will learn the basics of SwiftUI Combine, and then create a multipeer chat app using SwiftUI and Combine.
Building Content Recommendation Systems using MXNet GluonApache MXNet
Netflix competition triggered a flurry of research for recommendation engines. This presentation provides a survey of techniques and models for creating a recommender system. The presentation covers Matrix Factorisation, Factorisation Machines, Distributed Factorisation Machines, and DSSM networks as well provide code examples for developing a Matrix Factorisation in Gluon. At the end the presentation provides tips and tricks for large-scale, realtime recommender engines.
Build, Train & Deploy Your ML Application on Amazon SageMakerAmazon Web Services
This session covers a step by step walk through of a typical Machine Learning (ML) process: From asking the right questions, collecting the data, looking at the data, picking the right algorithms, training and evaluating ML models with Amazon SageMaker and bringing them live into production. A series of hands-on demos is included to illustrate these steps so that you can start building your first Machine Learning application right after this session.
by Michael St. Onge, Global Cloud Security Architect, AWS
Events are precursor to incidents, but how do you decide if an event is harmful? Tuning the signal to noise means that every event needs to be inspected and its impact calculated in as short amount of time as possible to stop bad things from happening. In this session, we will dive deep into a few event types to do advanced analysis in pursuit of deciding if it is a security incident, and how to resolve it by the time the alert hits your inbox.
Practical Guidance for Increasing your Serverless Application's SecurityChris Munns
Serverless applications are a hot topic today. By now, many are well versed in the benefits and uses for serverless, but there remain many misconceptions about serverless security.
Serverless applications bring with them numerous benefits, they also change the way that you think about building applications by changing up the way that data and customer requests move in and around them. As developers adopt fully managed services, the shared responsibility model between developers and cloud providers changes, and we argue that it changes for the better.
In this session we’ll cover how to think about security end to end of your serverless applications, from your code to the AWS services such as Amazon API Gateway and Amazon S3. We’ll talk about the importance of automated governance and how to best to organize your own processes for security first development.
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Enterprise customers who are planning to lift and shift out of a data center and move to AWS rely on discovery and migration tools for fast and reliable mass migrations. In this session, we show you how to quickly discover and asses your existing IT infrastructure using AWS Application Discovery Service. We also discuss best practices for automating discovery and migration execution. Finally, we dive deep into using AWS Migration Hub with AWS migration tools, like AWS Server Migration Service, and partner tools to track and execute application migrations at scale.
[NEW LAUNCH!] Introducing Amazon Elastic Inference: Reduce Deep Learning Infe...Amazon Web Services
Deploying deep learning applications at scale can be cost prohibitive due to the need for hardware acceleration to meet latency and throughput requirements of inference. Amazon Elastic Inference helps you tackle this problem by reducing the cost of inference by up to 75% with GPU-powered acceleration that can be right-sized to your application’s inference needs. In this session, learn about how to deploy TensorFlow, Apache MXNet, and ONNX models with Amazon Elastic Inference on Amazon EC2 and Amazon SageMaker. Hear from Autodesk on the positive impact of AI on tools used to design and make a better world. Learn about how Autodesk and the Autodesk AI Lab are using Amazon Elastic Inference to make it cost efficient to run these tools at scale.
Ever since we broke apart the front and back-end of our systems, we’ve longed to partially reunite them with a shared language. The benefits of code reuse and shared tooling are compelling but is this nirvana possible? In this session we will explore building both the front (mobile and web) and back-end of an application with a shared Kotlin codebase. You will learn how to setup the build, share code, and deploy the back-end as a serverless app.
ML Workflows with Amazon SageMaker and AWS Step Functions (API325) - AWS re:I...Amazon Web Services
Learn how you can build, train, and deploy machine learning workflows for Amazon SageMaker on AWS Step Functions. Learn how to stitch together services, such as AWS Glue, with your Amazon SageMaker model training to build feature-rich machine learning applications, and you learn how to build serverless ML workflows with less code. Cox Automotive also shares how it combined Amazon SageMaker and Step Functions to improve collaboration between data scientists and software engineers. We also share some new features to build and manage ML workflows even faster.
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.
This session will empower you to make the AWS machine learning tools an integral of your toolkit for turning data into intelligence. We will cover the pre-trained models available via AWS APIs as well as the new capabilities delivered by Amazon SageMaker. Learn how Amazon SageMaker empowers those of all experience levels to accelerate machine learning in your organisation
Speaker: Jenny Davies, Solutions Architect, AWS
Building Content Recommendation Systems Using Apache MXNet and Gluon - MCL402...Amazon Web Services
Recommendations are becoming an integral part of how many business serve customers, from targeted shopping on demand video. In this session, you’ll learn the key elements to build a recommendation system using Gluon, the new intuitive, dynamic programming interface for Apache MXNet. You’ll use matrix factorization techniques to build a video on-demand solution using deep learning.
Video anomaly detection using Amazon SageMaker, AWS DeepLens, & AWS IoT Green...Amazon Web Services
Anomaly detection, the art of finding rare events or observations that differ from the norm, has many applications. Anomaly detection is becoming increasingly popular due to Internet of Things (IoT) devices, whereby lots of data is generated but where manual labeling is not always feasible. In this chalk talk, we demonstrate how an ML model can be used to detect anomalous objects and uncharacteristic movements in video data. We discuss the implementation of the solution using Apache MXNet by deploying it to AWS DeepLens with Amazon SageMaker and AWS IoT Greengrass.
Building WhereML, an AI Powered Twitter Bot for Guessing Locations of Picture...Amazon Web Services
The WhereML Twitter bot is built on the LocationNet model which is trained with the Berkley Multimedia Commons public dataset of 33.9 million geotagged images from Flickr (and other sources). The model is based on a ResNet-101 architecture and adds a classification layer that splits the earth into ~15000 cells created with Google’s S2 spherical geometry library. This model is based on prior work completed at Berkley and Google.
In this session we’ll start by describing AI in general terms then diving into deep learning and the MXNet framework. We’ll describe the LocationNet model in detail and show how it is trained and created in Amazon SageMaker. Finally, we’ll talk about the Twitter Account Activity webhooks API and how to interact with it using an API Gateway and AWS Lambda function.
Attendees are encouraged to interact with the bot in real-time at whereml.bot or on twitter at @WhereML
All code used in this project is open source and was written live on twitch.tv/aws and attendees are encouraged to experiment with it.
Until now camera development has been very painful within android development. Although Camera2 API solved some of the problems in the original Camera API, however there were still lots of difficulties existed to write camera features. With the recent launch of JetPack CameraX support library, it aims to make camera app development easier by providing consistency and easy-to-use API that works on devices running Lollipop API-21 or above. In this talk, we will review main uses cases of CameraX Api which are preview, image analysis and image capture. We will also explore device-specific extensions such as portrait, HDR, night and beauty mode
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AI/ML is changing the way media companies produce and distribute content. In this workshop, we review many of the ways AI/ML can be applied throughout the entire media production and distribution process. We build an end-to-end metadata enrichment workflow that extracts meaningful metadata from content (audio, video, and images). We then build a solution that uses Amazon Rekognition, Amazon SageMaker, Amazon Transcribe, Amazon Comprehend, and Amazon Mechanical Turk to analyze content, and we use it to enrich a viewing experience and assist with compliance. We also build and train a custom object detection model, which will be used to augment the data that Amazon Rekognition provides. We cover all aspects of AI/ML-based metadata generation, from labeling a dataset, to training and hosting a model, all the way to validating the metadata prior to playout.
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Create Custom Resources to extend the support of resources and other extensions in CloudFormation. Build Custom Rules for cfn-lint to use the tool as a compliance control, and together with taskcat to fail fast.
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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Enterprise customers who are planning to lift and shift out of a data center and move to AWS rely on discovery and migration tools for fast and reliable mass migrations. In this session, we show you how to quickly discover and asses your existing IT infrastructure using AWS Application Discovery Service. We also discuss best practices for automating discovery and migration execution. Finally, we dive deep into using AWS Migration Hub with AWS migration tools, like AWS Server Migration Service, and partner tools to track and execute application migrations at scale.
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Deploying deep learning applications at scale can be cost prohibitive due to the need for hardware acceleration to meet latency and throughput requirements of inference. Amazon Elastic Inference helps you tackle this problem by reducing the cost of inference by up to 75% with GPU-powered acceleration that can be right-sized to your application’s inference needs. In this session, learn about how to deploy TensorFlow, Apache MXNet, and ONNX models with Amazon Elastic Inference on Amazon EC2 and Amazon SageMaker. Hear from Autodesk on the positive impact of AI on tools used to design and make a better world. Learn about how Autodesk and the Autodesk AI Lab are using Amazon Elastic Inference to make it cost efficient to run these tools at scale.
Ever since we broke apart the front and back-end of our systems, we’ve longed to partially reunite them with a shared language. The benefits of code reuse and shared tooling are compelling but is this nirvana possible? In this session we will explore building both the front (mobile and web) and back-end of an application with a shared Kotlin codebase. You will learn how to setup the build, share code, and deploy the back-end as a serverless app.
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Learn how you can build, train, and deploy machine learning workflows for Amazon SageMaker on AWS Step Functions. Learn how to stitch together services, such as AWS Glue, with your Amazon SageMaker model training to build feature-rich machine learning applications, and you learn how to build serverless ML workflows with less code. Cox Automotive also shares how it combined Amazon SageMaker and Step Functions to improve collaboration between data scientists and software engineers. We also share some new features to build and manage ML workflows even faster.
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In this session we’ll start by describing AI in general terms then diving into deep learning and the MXNet framework. We’ll describe the LocationNet model in detail and show how it is trained and created in Amazon SageMaker. Finally, we’ll talk about the Twitter Account Activity webhooks API and how to interact with it using an API Gateway and AWS Lambda function.
Attendees are encouraged to interact with the bot in real-time at whereml.bot or on twitter at @WhereML
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Until now camera development has been very painful within android development. Although Camera2 API solved some of the problems in the original Camera API, however there were still lots of difficulties existed to write camera features. With the recent launch of JetPack CameraX support library, it aims to make camera app development easier by providing consistency and easy-to-use API that works on devices running Lollipop API-21 or above. In this talk, we will review main uses cases of CameraX Api which are preview, image analysis and image capture. We will also explore device-specific extensions such as portrait, HDR, night and beauty mode
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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!
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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
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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.
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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.
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La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.