In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark’s ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL.
Session sponsored by Intel
Beyond Churn Prediction : An Introduction to uplift modelingPierre Gutierrez
These slides are from a talk I at the papis conference in Boston in 2016. The main subject is uplift modelling. Starting from a churn model approach for an e-gaming company, we introduce when to apply uplift methods, how to mathematically model them, and finally, how to evaluate them.
I tried to bridge the gap between causal inference theory and uplift theory, especially concerning how to properly cross validate the results. The notation used is the one from uplift modelling.
Using Deep Learning on Apache Spark to Diagnose Thoracic Pathology from Chest...Databricks
Overview and extended description: AI is expected to be the engine of technological advancements in the healthcare industry, especially in the areas of radiology and image processing. The purpose of this session is to demonstrate how we can build a AI-based Radiologist system using Apache Spark and Analytics Zoo to detect pneumonia and other diseases from chest x-ray images. The dataset, released by the NIH, contains around 110,00 X-ray images of around 30,000 unique patients, annotated with up to 14 different thoracic pathology labels. Stanford University developed a state-of-the-art model using CNN and exceeds average radiologist performance on the F1 metric. This talk focuses on how we can build a multi-label image classification model in a distributed Apache Spark infrastructure, and demonstrate how to build complex image transformations and deep learning pipelines using BigDL and Analytics Zoo with scalability and ease of use. Some practical image pre-processing procedures and evaluation metrics are introduced. We will also discuss runtime configuration, near-linear scalability for training and model serving, and other general performance topics.
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
Understanding how high powered ML models arrive at their predictions is an important aspect of Machine Learning, and SHAP is a powerful tool that enables practitioners to understand how different features combine to help a model arrive at a prediction.
This slidedeck is from a presentation given at pydata global on the theoretical foundations of SHAP as well as how to use its library. Link to the presentation can be found here: https://pydata.org/global2021/schedule/presentation/3/behind-the-black-box-how-to-understand-any-ml-model-using-shap/
Beyond Churn Prediction : An Introduction to uplift modelingPierre Gutierrez
These slides are from a talk I at the papis conference in Boston in 2016. The main subject is uplift modelling. Starting from a churn model approach for an e-gaming company, we introduce when to apply uplift methods, how to mathematically model them, and finally, how to evaluate them.
I tried to bridge the gap between causal inference theory and uplift theory, especially concerning how to properly cross validate the results. The notation used is the one from uplift modelling.
Using Deep Learning on Apache Spark to Diagnose Thoracic Pathology from Chest...Databricks
Overview and extended description: AI is expected to be the engine of technological advancements in the healthcare industry, especially in the areas of radiology and image processing. The purpose of this session is to demonstrate how we can build a AI-based Radiologist system using Apache Spark and Analytics Zoo to detect pneumonia and other diseases from chest x-ray images. The dataset, released by the NIH, contains around 110,00 X-ray images of around 30,000 unique patients, annotated with up to 14 different thoracic pathology labels. Stanford University developed a state-of-the-art model using CNN and exceeds average radiologist performance on the F1 metric. This talk focuses on how we can build a multi-label image classification model in a distributed Apache Spark infrastructure, and demonstrate how to build complex image transformations and deep learning pipelines using BigDL and Analytics Zoo with scalability and ease of use. Some practical image pre-processing procedures and evaluation metrics are introduced. We will also discuss runtime configuration, near-linear scalability for training and model serving, and other general performance topics.
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
Understanding how high powered ML models arrive at their predictions is an important aspect of Machine Learning, and SHAP is a powerful tool that enables practitioners to understand how different features combine to help a model arrive at a prediction.
This slidedeck is from a presentation given at pydata global on the theoretical foundations of SHAP as well as how to use its library. Link to the presentation can be found here: https://pydata.org/global2021/schedule/presentation/3/behind-the-black-box-how-to-understand-any-ml-model-using-shap/
The Scout24 Data Platform (A Technical Deep Dive)RaffaelDzikowski
The Scout24 Data Platform powers all reporting, ad hoc analytics and machine learning products at AutoScout24 and ImmobilienScout24. In this talk, we will take a technical deep dive into our modern, cloud-based big data platform. We will discuss our evolution of approaches to ingestion, ETL, access control, reporting, and machine learning with a focus on in-the-trenches learnings gained from our many failures and successes as we migrated from a traditional Oracle Data Warehouse to an AWS-based data lake.
These slides are from a talk I gave at Google Campus Madrid for the Machine Learning Meetup. The main subject is uplift modelling. Starting from a churn model approach for an e-gaming company, we introduce when to apply uplift methods, how to mathematically model them, and finally, how to evaluate them.
With more than 700 million monthly active users, Instagram continues to make it easier for people across the globe to join the community, share their experiences, and strengthen connections to their friends and passions. Powering Instagram’s various products requires the use of machine learning, high performance ranking services, and most importantly large amounts of data. At Instagram, we use Apache Spark for several critical production pipelines, including generating labeled training data for our machine learning models. In this session, you’ll learn about how one of Instagram’s largest Spark pipelines has evolved over time in order to process ~300 TB of input and ~90 TB of shuffle data. We’ll discuss the experience of building and managing such a large production pipeline and some tips and tricks we’ve learned along the way to manage Spark at scale. Topics include migrating from RDD to Dataset for better memory efficiency, splitting up long-running pipelines in order to better tune intermediate shuffle data, and dealing with changing data skew over time. Finally, we will also go over some optimizations we have made in order to maintain reliability of this critical data pipeline.
Got data?… now what? An introduction to modern data platformsJamesAnderson599331
What are Data Analytics Platforms? What decision points are necessary in creating a modern, unified analytics data platform? What benefits are there to building your analytics data platform on Google Cloud Platform? Susan Pierce walks us through it all.
CI/CD Templates: Continuous Delivery of ML-Enabled Data Pipelines on DatabricksDatabricks
Data & ML projects bring many new complexities beyond the traditional software development lifecycle. Unlike software projects, after they were successfully delivered and deployed, they cannot be abandoned but must be continuously monitored if model performance still satisfies all requirements. We can always get new data with new statistical characteristics that can break our pipelines or influence model performance. All these qualities of data & ML projects lead us to the necessity of continuous testing and monitoring of our models and pipelines.
50k runs, millions of metrics, parameters or tags, some bursts at 20k QPS. That’s the volume of data managed by our MLflow tracking servers this year at Criteo. In this talk, you will learn how we set up a shared instance of MLflow at company scale. We will present our contributions to the SQLAlchemyStore to make it responsive at this scale. We will present you how we turned MLflow to a production-ready system. How we scaled horizontally a shared instance on a mesos cluster ? Our monitoring system based on prometheus. Integration to the company Single Sign-On (SSO) authentication. And how our data scientists register their runs from the largest hadoop cluster in Europe.
The Value of the Modern Data Architecture with Apache Hadoop and Teradata Hortonworks
This webinar discusses why Apache Hadoop most typically the technology underpinning "Big Data". How it fits in a modern data architecture and the current landscape of databases and data warehouses that are already in use.
Data-Centric Business Transformation Using Knowledge GraphsAlan Morrison
From a talk at the Data Architecture Summit in Chicago in 2018--reviews digital transformation and what deep transformation really implies at the data layer. Cross-enterprise knowledge graphs are becoming feasible and can be a key enabler of deep transformation.
Le Big data en santé et l'éthique, sont- ils compatibles ?Céline Poirier
La caractéristique commune à l'innovation en e-santé et en santé mobile est qu’elles nous conduisent vers une médecine non plus curative mais préventive, voire prédictive. Pour y parvenir, la e-santé doit s’appuyer sur la génération, le partage (Open Data), et le traitement d'une multitude de données grâce aux outils du Big Data. Mais jusqu’où pouvons- nous aller ?
Les risques d’une trop grande divulgation des données de santé sont-ils supérieurs aux avantages ? Comment redonner confiance aux français pour faire avancer la santé connectée ?
Soutenance de ma thèse professionnelle du MBAMCI en février 2016
Application de l’intelligence artificielle à la prédiction et au diagnostic e...Alain Tassy
Le cancer est en forte croissance dans le monde comme en france (385 000 nouveaux cas en 2016). Son traitement fait appel à de nombreux intervenants et le partage des information est insuffisant. On assiste à une explosion des données générées pour le traitement.
Le machine learning en médecine va permettre de dépasser les capacité cognitives humaines (traitement 5 facteurs maximum en même temps). Il va permettre d'améliorer le diagnostique et la prédiction qui amènera une personnalisation des traitements.
Le projet Big Data pour la radiothérapie est soutenu par BPI et implique entre autre plusieurs hopitaux, Atos et Télécom Paristech. Il est original car les données resteront dans les hôpitaux et ce sont les algorithmes qui vont apprendre localement.
Poisoning attacks on Federated Learning based IoT Intrusion Detection SystemSai Kiran Kadam
Attacks on federated learning model are discussed as a part of my research to build a model that overcomes the diverse security issues and vulnerabilities in the cloud in the process of building a unified machine learning model that can benefit multi-user/ multi-companies to work together.
Productionizing Machine Learning Pipelines with Databricks and Azure MLDatabricks
Deployment of modern machine learning applications can require a significant amount of time, resources, and experience to design and implement – thus introducing overhead for small-scale machine learning projects.
BigDL Deep Learning in Apache Spark - AWS re:invent 2017Dave Nielsen
In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark’s ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL. Presentation by Alex Kalinin, Tim Fox, Sujee Maniyam & Dave Nielsen at re:invent.
AWS makes it easy to migrate databases to the cloud and then operate them, faster and more cost-effectively. Our database capabilities also enable a number of methods to protect database volumes, and this session will help you understand best practices for backing up database instances in the cloud and then storing them in S3 for durable and available storage.
The Scout24 Data Platform (A Technical Deep Dive)RaffaelDzikowski
The Scout24 Data Platform powers all reporting, ad hoc analytics and machine learning products at AutoScout24 and ImmobilienScout24. In this talk, we will take a technical deep dive into our modern, cloud-based big data platform. We will discuss our evolution of approaches to ingestion, ETL, access control, reporting, and machine learning with a focus on in-the-trenches learnings gained from our many failures and successes as we migrated from a traditional Oracle Data Warehouse to an AWS-based data lake.
These slides are from a talk I gave at Google Campus Madrid for the Machine Learning Meetup. The main subject is uplift modelling. Starting from a churn model approach for an e-gaming company, we introduce when to apply uplift methods, how to mathematically model them, and finally, how to evaluate them.
With more than 700 million monthly active users, Instagram continues to make it easier for people across the globe to join the community, share their experiences, and strengthen connections to their friends and passions. Powering Instagram’s various products requires the use of machine learning, high performance ranking services, and most importantly large amounts of data. At Instagram, we use Apache Spark for several critical production pipelines, including generating labeled training data for our machine learning models. In this session, you’ll learn about how one of Instagram’s largest Spark pipelines has evolved over time in order to process ~300 TB of input and ~90 TB of shuffle data. We’ll discuss the experience of building and managing such a large production pipeline and some tips and tricks we’ve learned along the way to manage Spark at scale. Topics include migrating from RDD to Dataset for better memory efficiency, splitting up long-running pipelines in order to better tune intermediate shuffle data, and dealing with changing data skew over time. Finally, we will also go over some optimizations we have made in order to maintain reliability of this critical data pipeline.
Got data?… now what? An introduction to modern data platformsJamesAnderson599331
What are Data Analytics Platforms? What decision points are necessary in creating a modern, unified analytics data platform? What benefits are there to building your analytics data platform on Google Cloud Platform? Susan Pierce walks us through it all.
CI/CD Templates: Continuous Delivery of ML-Enabled Data Pipelines on DatabricksDatabricks
Data & ML projects bring many new complexities beyond the traditional software development lifecycle. Unlike software projects, after they were successfully delivered and deployed, they cannot be abandoned but must be continuously monitored if model performance still satisfies all requirements. We can always get new data with new statistical characteristics that can break our pipelines or influence model performance. All these qualities of data & ML projects lead us to the necessity of continuous testing and monitoring of our models and pipelines.
50k runs, millions of metrics, parameters or tags, some bursts at 20k QPS. That’s the volume of data managed by our MLflow tracking servers this year at Criteo. In this talk, you will learn how we set up a shared instance of MLflow at company scale. We will present our contributions to the SQLAlchemyStore to make it responsive at this scale. We will present you how we turned MLflow to a production-ready system. How we scaled horizontally a shared instance on a mesos cluster ? Our monitoring system based on prometheus. Integration to the company Single Sign-On (SSO) authentication. And how our data scientists register their runs from the largest hadoop cluster in Europe.
The Value of the Modern Data Architecture with Apache Hadoop and Teradata Hortonworks
This webinar discusses why Apache Hadoop most typically the technology underpinning "Big Data". How it fits in a modern data architecture and the current landscape of databases and data warehouses that are already in use.
Data-Centric Business Transformation Using Knowledge GraphsAlan Morrison
From a talk at the Data Architecture Summit in Chicago in 2018--reviews digital transformation and what deep transformation really implies at the data layer. Cross-enterprise knowledge graphs are becoming feasible and can be a key enabler of deep transformation.
Le Big data en santé et l'éthique, sont- ils compatibles ?Céline Poirier
La caractéristique commune à l'innovation en e-santé et en santé mobile est qu’elles nous conduisent vers une médecine non plus curative mais préventive, voire prédictive. Pour y parvenir, la e-santé doit s’appuyer sur la génération, le partage (Open Data), et le traitement d'une multitude de données grâce aux outils du Big Data. Mais jusqu’où pouvons- nous aller ?
Les risques d’une trop grande divulgation des données de santé sont-ils supérieurs aux avantages ? Comment redonner confiance aux français pour faire avancer la santé connectée ?
Soutenance de ma thèse professionnelle du MBAMCI en février 2016
Application de l’intelligence artificielle à la prédiction et au diagnostic e...Alain Tassy
Le cancer est en forte croissance dans le monde comme en france (385 000 nouveaux cas en 2016). Son traitement fait appel à de nombreux intervenants et le partage des information est insuffisant. On assiste à une explosion des données générées pour le traitement.
Le machine learning en médecine va permettre de dépasser les capacité cognitives humaines (traitement 5 facteurs maximum en même temps). Il va permettre d'améliorer le diagnostique et la prédiction qui amènera une personnalisation des traitements.
Le projet Big Data pour la radiothérapie est soutenu par BPI et implique entre autre plusieurs hopitaux, Atos et Télécom Paristech. Il est original car les données resteront dans les hôpitaux et ce sont les algorithmes qui vont apprendre localement.
Poisoning attacks on Federated Learning based IoT Intrusion Detection SystemSai Kiran Kadam
Attacks on federated learning model are discussed as a part of my research to build a model that overcomes the diverse security issues and vulnerabilities in the cloud in the process of building a unified machine learning model that can benefit multi-user/ multi-companies to work together.
Productionizing Machine Learning Pipelines with Databricks and Azure MLDatabricks
Deployment of modern machine learning applications can require a significant amount of time, resources, and experience to design and implement – thus introducing overhead for small-scale machine learning projects.
BigDL Deep Learning in Apache Spark - AWS re:invent 2017Dave Nielsen
In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark’s ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL. Presentation by Alex Kalinin, Tim Fox, Sujee Maniyam & Dave Nielsen at re:invent.
AWS makes it easy to migrate databases to the cloud and then operate them, faster and more cost-effectively. Our database capabilities also enable a number of methods to protect database volumes, and this session will help you understand best practices for backing up database instances in the cloud and then storing them in S3 for durable and available storage.
Data Modelling is an important tool in the toolbox of a developer. By building and communicating a shared understanding of the domain they're working with, their applications and APIs are more useable and maintainable. However, as you scale up your technical teams, how do you keep these benefits whilst avoiding time-consuming meetings every time something new comes along? This talk reminds ourselves of key data modelling technique and how our use of Kafka changes and informs them. It then examines how these patterns change as more teams join your organisation and how Kafka comes into its own in this world.
Create an IoT Gateway and Establish a Data Pipeline to AWS IoT with Intel - I...Amazon Web Services
In this session, learn how to create a complete Gateway-based IoT framework – from the edge to the cloud and back. By using an IoT Gateway as a central data collection, processing, and communication hub, you can create IoT connectivity without having to replace legacy hardware. We show you how to use an Intel NUC gateway and Arduino 101 sensor hub to gather environmental data, and step you through establishing a data pipeline to AWS IoT. We use AWS Lambda to create a rules engine for your data, and then send a control signal back down the Intel Gateway. Bring your laptop and your AWS account for this workshop.
by Drew Meyer, Sr. Manager, Product Marketing AWS
We will cover the core AWS storage services, which include Amazon Simple Storage Service (Amazon S3), Amazon Glacier, Amazon Elastic File System (Amazon EFS), and Amazon Elastic Block Store (Amazon EBS). We also discuss data transfer services such as AWS Snowball, Snowball Edge, and AWS Snowmobile, and hybrid storage solutions such as AWS Storage Gateway.
Building Text Analytics Applications on AWS using Amazon Comprehend - AWS Onl...Amazon Web Services
Learning Objectives:
- Get an introduction to Natural Language Processing (NLP)
- Learn benefits of new approaches to analytics and technologies that help empower better decisions, e.g., NLP, data prep
- Build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service in a step by step demo
Open Source at AWS: Code, Contributions, Collaboration, and CommunicationAmazon Web Services
At OSCON 2018, Adrian Cockcroft detailed the many ways AWS participates in open source: contributing to open source projects, reporting bugs, contributing fixes and enhancements to a wide spectrum of projects ranging from the Linux kernel to PostgreSQL and Kubernetes, and managing the hundreds of projects of its own.
Accelerate machine-learning workloads using Amazon EC2 P3 instances - CMP204 ...Amazon Web Services
Organizations are tackling exponentially complex questions across advanced scientific, energy, high-tech, and medical fields. Machine learning (ML) makes it possible to quickly explore a multitude of scenarios and generate the best answers, ranging from image, video, and speech recognition to autonomous vehicle systems and weather prediction. In this interactive chalk talk, we discuss the latest advancements in compute to support your ML goals. We also discuss how, for data scientists, researchers, and developers who want to speed development of their ML applications, Amazon Elastic Compute Cloud (Amazon EC2) P3 instances are the most powerful, cost-effective, and versatile GPU-compute instances available.
Slides: How Automating Data Lineage Improves BI PerformanceDATAVERSITY
BI landscapes are becoming increasingly complex with the surge in adoption of cloud technologies. Your BI group may have one foot in legacy systems and the other in more modern cloud-based systems, and this alone makes managing and understanding your data virtually impossible.
From needing to understand the impact of a change in a source system from the ETL through to reporting, to finding the source of a reporting error that an end-user questioned you on, to quickly responding to auditors’ demands – these recurring daily BI tasks and more turn into weeks-long projects.
Join us for our upcoming webinar where you’ll learn:
• How to enable your BI group to fix problems sooner for quicker access to accurate data
• The advantages of moving from manual to automated data lineage
• Use cases for BI and analytics groups in a variety of industries, including finance and insurance
Serverless Text Analytics with Amazon ComprehendDonnie Prakoso
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text.
This deck provides how to build your own text analytics using Amazon Comprehend and integration with other AWS services. On top of that, this deck also provides an introduction to Amazon Lex.
What it Means to be a Next-Generation Managed Service ProviderDatadog
Webinar that took place on July 12 2017.
The emergence of cloud-based infrastructure has dramatically reshaped
the IT landscape for managed service providers and their customers. Infrastructure is now dynamic, elastic, and instantly available to any individual or organization.
Customers are becoming increasingly aware of the value of cloud services, and with this heightened awareness comes the desire to partner with providers who can guide them toward innovative business solutions and high-performance environments. But in this new landscape, gaining insight into the status and performance of dynamic infrastructure and applications is more challenging than ever.
Join us as we host Thomas Robinson, Solutions Architect at Amazon Web Services, and Patrick Hannah, VP of Engineering at CloudHesive, to discuss what it means to be a next-generation managed service provider and how Datadog provides visibility into modern cloud infrastructure and helps you adopt new approaches to remain competitive in this ever-changing environment.
Over 90% of today’s data has been generated in the last two years, and growth rates continue to climb. In this session, we’ll step through challenges and best practices with data capturing, how to derive meaningful insights to help predict the future, and common pitfalls in data analysis.
Come discover how integrated solutions involving Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon Machine Learning/Deep Learning result in effective data systems for data scientists and business users, alike.
NEW LAUNCH! Natural Language Processing for Data Analytics - MCL343 - re:Inve...Amazon Web Services
The need for Natural Language Processing (NLP) is gaining more importance as the amount of unstructured text data doubles every 18 months and customers are looking to extend their existing analytics workloads to include natural language capabilities. Historically, this data had been prohibitively expensive to store and early manual processing evolved into rule-based systems, which were expensive to operate and inflexible. In this session we will show you how you can address this problem using Amazon Comprehend.
GPSBUS216-GPS Applying AI-ML to Find Security Needles in the HaystackAmazon Web Services
Security is about visibility and control. It starts with getting visibility (collecting as much data as possible about your environment), then deciding what is worth alarming versus what is a distraction. A classic case of finding needles in the haystack. AWS Partners can leverage highly scalable, machine learning (ML) services to process large amounts of log, event, flow, and other data to build AWS–specific security solutions that scale. Pass the undifferentiated heavy lifting to AWS so you can focus on your core value proposition! This session helps AWS Partners understand what services are available and applicable for building security solutions, and provides use cases to help accelerate adoption.
GAM311-How Linden Lab Built a Virtual World on the AWS Cloud.pdfAmazon Web Services
Linden Lab has spent over a decade optimizing the production operations for Second Life, an online 3D virtual world created by its users. With our new social VR platform, Sansar, we wanted to take our vision of virtual experiences to a whole new level of innovation in which AWS played a vital role. We’ll dive into Sansar's AWS tech stack, an infrastructure built not only for technical robustness but also extreme scalability. We will discuss what the different use cases were between EC2 and Containers and how Lambda worked as a positive enabler for customization. Lastly, we’ll cover IAM, Security Groups and VPC, which we refer collectively as the "Great Wall of Preventing Unfortunate Design Decisions.”.
Similar to BigDL: Image Recognition Using Apache Spark with BigDL - MCL358 - re:Invent 2017 (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
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