Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. Apache MXNet is a fully-featured, flexibly-programmable and ultra-scalable deep learning framework supporting innovative deep models including convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). This Tech Talk will show you how to launch the deep learning cloud formation template and deploy the deep learning AMI to train your own deep neural network, using MNIST, to recognize handwritten digits and test it for accuracy.
Learning Objectives:
- Learn about the features and benefits of Apache MXNet
- Learn about the deep learning AMIs with the tools you need for DL
- Learn how to train a neural network using MXNet
A Deeper Dive into Apache MXNet - March 2017 AWS Online Tech TalksAmazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. Apache MXNet is a fully-featured, flexibly-programmable and ultra-scalable deep learning framework supporting innovative deep models including convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). This Tech Talk will show you how to launch the deep learning cloud formation template and deploy the deep learning AMI to train your own deep neural network, using MNIST, to recognize handwritten digits and test it for accuracy.
Learning Objectives:
- Learn about the features and benefits of Apache MXNet
- Learn about the deep learning AMIs with the tools you need for DL
- Learn how to train a neural network using MXNet"
Amazon Elasticache Deep Dive - March 2017 AWS Online Tech TalksAmazon Web Services
Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases. In this tech talk, we’ll provide a peek behind the scenes to learn about Amazon ElastiCache's design and architecture. You’ll see common design patterns with our Redis and Memcached offerings and how customers have used them for in-memory operations to reduce latency and improve application throughput. During this session, we review ElastiCache best practices, design patterns, and anti-patterns.
Learning Objectives:
- Learn how to integrate Amazon ElastiCache in your workloads
- Understand the benefits of an In-Memory data store
- Learn how to apply various caching strategies in your applications
- Hands on demonstration using Amazon ElastiCache
Scalable Deep Learning on AWS Using Apache MXNet - AWS Summit Tel Aviv 2017Amazon Web Services
Artificial Intelligence (AI) and deep learning are now ready to power your business, as it is powering most of the innovation of Amazon.com with autonomous drones, and robots, Amazon Alexa, Amazon Go, and many other hard and important business problems. Come and learn why and how to get started with deep learning, and what you can expect from a future with better AI in the cloud and on the edge.
AWS provides a wide set of services to manage your data, which allow our customers to choose the right tool to the right workload. Learn how to make your databases up to 10x faster and less expensive with Amazon ElastiCache for Redis and utilize DynamoDB Accelerator (DAX) to access your data on DynamoDB faster with no additional development efforts. If you need fast access to your data, these services might be the right services for your workload.
AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)Amazon Web Services
Algorithmia is a startup with a mission to make state of the art machine learning discoverable by everyone&emdash;they offer the largest algorithm marketplace in the world, with over 2500 algorithms supporting tens of thousands of application developers. Algorithma is the first company to make deep learning, one of the most conceptually difficult areas of computing, accessible to any company via microservices. In this session, you learn how this startup has selected and optimized Amazon EC2 instances for various algorithms (including the latest generation of GPU optimized instances), to create a flexible and scalable platform. They also share their architecture and best practices for getting any computationally-intensive application started quickly.
BDA 302 Deep Dive on Migrating Big Data Workloads to Amazon EMRAmazon Web Services
Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premise deployments to Amazon EMR in order to save costs, increase availability, and improve performance. Amazon EMR is a managed service that lets you process and analyze extremely large data sets using the latest versions of over 15 open-source frameworks in the Apache Hadoop and Spark ecosystems. This session will focus on identifying the components and workflows in your current environment and providing the best practices to migrate these workloads to Amazon EMR. We will explain how to move from HDFS to Amazon S3 as a durable storage layer, and how to lower costs with Amazon EC2 Spot instances and Auto Scaling. Additionally, we will go over common security recommendations and tuning tips to accelerate the time to production.
Building HPC Clusters as Code in the (Almost) Infinite Cloud | AWS Public Sec...Amazon Web Services
Every day, the computing power of high-performance computing (HPC) clusters helps scientists make breakthroughs, such as proving the existence of gravitational waves and screening new compounds for new drugs. Yet building HPC clusters is out of reach for most organizations, due to the upfront hardware costs and ongoing operational expenses. Now the speed of innovation is only bound by your imagination, not your budget. Researchers can run one cluster for 10,000 hours or 10,000 clusters for one hour anytime, from anywhere, and both cost the same in the cloud. And with the availability of Public Data Sets in Amazon S3, petabyte scale data is instantly accessible in the cloud. Attend and learn how to build HPC clusters on the fly, leverage Amazon’s Spot market pricing to minimize the cost of HPC jobs, and scale HPC jobs on a small budget, using all the same tools you use today, and a few new ones too.
Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; Deployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively.
A Deeper Dive into Apache MXNet - March 2017 AWS Online Tech TalksAmazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. Apache MXNet is a fully-featured, flexibly-programmable and ultra-scalable deep learning framework supporting innovative deep models including convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). This Tech Talk will show you how to launch the deep learning cloud formation template and deploy the deep learning AMI to train your own deep neural network, using MNIST, to recognize handwritten digits and test it for accuracy.
Learning Objectives:
- Learn about the features and benefits of Apache MXNet
- Learn about the deep learning AMIs with the tools you need for DL
- Learn how to train a neural network using MXNet"
Amazon Elasticache Deep Dive - March 2017 AWS Online Tech TalksAmazon Web Services
Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases. In this tech talk, we’ll provide a peek behind the scenes to learn about Amazon ElastiCache's design and architecture. You’ll see common design patterns with our Redis and Memcached offerings and how customers have used them for in-memory operations to reduce latency and improve application throughput. During this session, we review ElastiCache best practices, design patterns, and anti-patterns.
Learning Objectives:
- Learn how to integrate Amazon ElastiCache in your workloads
- Understand the benefits of an In-Memory data store
- Learn how to apply various caching strategies in your applications
- Hands on demonstration using Amazon ElastiCache
Scalable Deep Learning on AWS Using Apache MXNet - AWS Summit Tel Aviv 2017Amazon Web Services
Artificial Intelligence (AI) and deep learning are now ready to power your business, as it is powering most of the innovation of Amazon.com with autonomous drones, and robots, Amazon Alexa, Amazon Go, and many other hard and important business problems. Come and learn why and how to get started with deep learning, and what you can expect from a future with better AI in the cloud and on the edge.
AWS provides a wide set of services to manage your data, which allow our customers to choose the right tool to the right workload. Learn how to make your databases up to 10x faster and less expensive with Amazon ElastiCache for Redis and utilize DynamoDB Accelerator (DAX) to access your data on DynamoDB faster with no additional development efforts. If you need fast access to your data, these services might be the right services for your workload.
AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)Amazon Web Services
Algorithmia is a startup with a mission to make state of the art machine learning discoverable by everyone&emdash;they offer the largest algorithm marketplace in the world, with over 2500 algorithms supporting tens of thousands of application developers. Algorithma is the first company to make deep learning, one of the most conceptually difficult areas of computing, accessible to any company via microservices. In this session, you learn how this startup has selected and optimized Amazon EC2 instances for various algorithms (including the latest generation of GPU optimized instances), to create a flexible and scalable platform. They also share their architecture and best practices for getting any computationally-intensive application started quickly.
BDA 302 Deep Dive on Migrating Big Data Workloads to Amazon EMRAmazon Web Services
Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premise deployments to Amazon EMR in order to save costs, increase availability, and improve performance. Amazon EMR is a managed service that lets you process and analyze extremely large data sets using the latest versions of over 15 open-source frameworks in the Apache Hadoop and Spark ecosystems. This session will focus on identifying the components and workflows in your current environment and providing the best practices to migrate these workloads to Amazon EMR. We will explain how to move from HDFS to Amazon S3 as a durable storage layer, and how to lower costs with Amazon EC2 Spot instances and Auto Scaling. Additionally, we will go over common security recommendations and tuning tips to accelerate the time to production.
Building HPC Clusters as Code in the (Almost) Infinite Cloud | AWS Public Sec...Amazon Web Services
Every day, the computing power of high-performance computing (HPC) clusters helps scientists make breakthroughs, such as proving the existence of gravitational waves and screening new compounds for new drugs. Yet building HPC clusters is out of reach for most organizations, due to the upfront hardware costs and ongoing operational expenses. Now the speed of innovation is only bound by your imagination, not your budget. Researchers can run one cluster for 10,000 hours or 10,000 clusters for one hour anytime, from anywhere, and both cost the same in the cloud. And with the availability of Public Data Sets in Amazon S3, petabyte scale data is instantly accessible in the cloud. Attend and learn how to build HPC clusters on the fly, leverage Amazon’s Spot market pricing to minimize the cost of HPC jobs, and scale HPC jobs on a small budget, using all the same tools you use today, and a few new ones too.
Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; Deployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively.
AWS re:Invent 2016: Deep Dive: Amazon EMR Best Practices & Design Patterns (B...Amazon Web Services
Amazon EMR is one of the largest Hadoop operators in the world. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost-efficient. Finally, we dive into some of our recent launches to keep you current on our latest features. This session will feature Asurion, a provider of device protection and support services for over 280 million smartphones and other consumer electronics devices. Asurion will share how they architected their petabyte-scale data platform using Apache Hive, Apache Spark, and Presto on Amazon EMR.
Slide-deck used in Bend Web Design and Development Meetup (http://web.archive.org/web/20150728021205/http://www.meetup.com/Bend-Web-Design-and-Development/events/222592014/)
"In this session, you will learn how to easily access your data on S3, and how to visualize and generate insights from Amazon Athena and other data sources through Amazon QuickSight. In addition we will share some tips & best practices for using Athena & QuickSight.
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.
Amazon QuickSight is a fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from various data sources (Amazon Redshift, Amazon Athena, Amazon EMR, Amazon RDS and more)."
Strategic Uses for Cost Efficient Long-Term Cloud StorageAmazon Web Services
Compared to storing long-term datasets on-premises, archiving in the cloud is a smart alternative whether you’re looking for an active archive solution, tape replacement, or to fulfill a compliance requirement. Learn how AWS customers are simplifying their archiving strategy and meeting compliance needs using Amazon Glacier. Hear how customers have evolved their backup and disaster recovery architectures and replaced tape solutions by turning to AWS for a more cost efficient, durable and agile solution. We will showcase Sony DADC's active archive deployment on Glacier and demo how some of our financial service customers have set up compliant archives to meet their regulatory objectives.
(BDT305) Lessons Learned and Best Practices for Running Hadoop on AWS | AWS r...Amazon Web Services
Enterprises are starting to deploy large scale Hadoop clusters to extract value out of the data that they are generating. These clusters often span hundreds of nodes. To speed up the time to value, a lot of the newer deployments are happening in AWS, moving from the traditional on-premises, bare-metal world. Cloudera supports just such deployments. In this session, Cloudera shares the lessons learned and best practices for deploying multi-tenant Hadoop clusters in AWS. They will cover what reference deployments look like, what services are relevant for Hadoop deployments, network configurations, instance types, backup and disaster recovery considerations, and security considerations. They will also talk about what works well, what doesn't, and what has to be done going forward to improve the operability of Hadoop on AWS.
More and more, the scalable on-demand infrastructure provided by AWS is being used by researchers, scientists and engineers in Life Sciences, Finance and Engineering to solve bigger problems, answer complex questions and run larger simulations. In this session we start by talking about the supercomputing class performance and high performance storage available to the scientists and engineers at their fingertips. We will go over examples of how startups are innovating and large enterprises are extending their HPC environments. Finally, we walk through some of the common questions that come up as organizations start leveraging AWS for their high performance computing needs.
Deep Learning for Data Scientists: Using Apache MXNet and R on AWS - June 201...Amazon Web Services
Learning Objectives:
- Deploy a Data science environment in minutes with the AWS -
- Deep Learning AMI
- Getting started with Apache MXNet on R
- Train and deploy Deep Learning models at scale with R
Deep Learning (DL) is a subset of Machine Learning (ML) that extends the concept of Artificial Neural Networks (ANN) to uncover hidden patterns in unstructured datasets. Due to the current ubiquity of data (Big Data), and availability of on-demand, inexpensive, and parallel hardware such as Graphics Processing Units (GPUs) on Amazon EC2, Deep Learning has revitalized the excitement in Artificial Intelligence. Breakthrough results can be seen in industry applications such, computer vision, robotics, healthcare, security, retail, and more. Apache MXNet is a fully-featured, flexibly-programmable and ultra-scalable deep learning framework supporting state-of-the-art deep models including convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). MXNet enables Data Scientists familiar with the R programing language to train and deploy deep models at scale, using their favorite language, with the same fast performance observed by Python, Scala or C++ ML practitioners.
You will also hear from Jared P. Lander, adjunct professor of statistics at Columbia University and the organizer of the New York Open Statistical Programming Meetup—the world’s largest R meetup—and the New York R Conference.
Participants will learn how to spin up a pre-built, GPU enabled Data Science environment using the AWS Deep Learning Amazon Machine Image (AMI), in few minutes. We will write a deep learning program with MXNet in a few lines of codes using the R programming language. We will discuss training deep learning models on one or multiple GPUs via R. Finally, we will compare deep models to some traditional Machine Learning models such as Support Vector Machines or Random Forest.
Visit http:aws.amazon.com/hpc for more information about HPC on AWS.
High Performance Computing (HPC) allows scientists and engineers to solve complex science, engineering, and business problems using applications that require high bandwidth, low latency networking, and very high compute capabilities. AWS allows you to increase the speed of research by running high performance computing in the cloud and to reduce costs by providing Cluster Compute or Cluster GPU servers on-demand without large capital investments. You have access to a full-bisection, high bandwidth network for tightly-coupled, IO-intensive workloads, which enables you to scale out across thousands of cores for throughput-oriented applications.
Tune your Big Data Platform to Work at Scale: Taking Hadoop to the Next Level...Amazon Web Services
Learn how to set up a highly scalable, robust, and secure Hadoop platform using Amazon EMR. We'll perform a demonstration using a 100-node Amazon EMR cluster and take you through the best practices and performance tuning required for different workloads to ensure they are production ready.
Speaker: Amo Abeyaratne, Big Data Consultant, Amazon Web Services
Featured Customer - Ambidata
Consolidate MySQL Shards Into Amazon Aurora Using AWS Database Migration Serv...Amazon Web Services
If you’re running a MySQL database at scale, there’s a good chance you’re sharding your database deployment. Sharding is a useful way to increase the scale of your deployment, but it has drawbacks like higher costs, high administration overheard and lower elasticity. It’s harder to grow or shrink a sharded database deployment to match your traffic patterns. In this session, we will discuss and demonstrate how to use AWS Database Migration Service to consolidate multiple MySQL shards into an Amazon Aurora cluster to reduce cost, improve elasticity and make it easier to manage your database.
Learning Objectives:
Learn how to scale your MySQL database at reduced cost and higher elasticity, by consolidating multiple shards into one Amazon Aurora cluster.
Amazon Elastic MapReduce Deep Dive and Best Practices (BDT404) | AWS re:Inven...Amazon Web Services
Amazon Elastic MapReduce is one of the largest Hadoop operators in the world. Since its launch four years ago, our customers have launched more than 5.5 million Hadoop clusters. In this talk, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
AWS re:Invent 2016: How to Scale and Operate Elasticsearch on AWS (DEV307)Amazon Web Services
Elasticsearch has quickly become the leading open source technology for scaling search and building document services on. Many software providers have come to rely on it to serve the needs of high-performance, production applications.
In this talk, we’ll go deep on lessons learned from three years in production scaling from a few shards to more than 100 spread across 100s of nodes on AWS--to serve real-time queries against 100s of millions of documents.
Attendees will learn:
* How to capacity plan for ES on AWS
* How to scale and reshard on AWS with zero downtime
* What AWS and ES metrics to collect and alert on
* Tips on day to day ES operations
Session sponsored by SignalFx.
(BDT208) A Technical Introduction to Amazon Elastic MapReduceAmazon Web Services
"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way.
In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."
Best Practices for Running Amazon EC2 Spot Instances with Amazon EMR - AWS On...Amazon Web Services
Learning Objectives:
- Learn how to run Amazon EMR clusters on Spot instances and significantly reduce the cost of processing vast amounts of data on managed Hadoop clusters
- Understand key EC2 Spot Instances concepts and common usage patterns for maximum scale and cost optimization for Big Data workloads
- See a few customer examples that show how to leverage the full scale of the AWS cloud for faster results
Those who out-compute can many times out-compete. The cloud gives you access to a massive amount of compute power when you need it. This talk will present an introduction to HPC in the cloud, including, the benefits of HPC in the cloud, how to get started, some tools to use, and how you can manage data. We will showcase several examples of HPC in the cloud by a number of public sector and commercial customers.
Created by: Dr. Jeff Layton, Principal, Solutions Architect
ElastiCache Deep Dive: Best Practices and Usage Patterns - March 2017 AWS Onl...Amazon Web Services
Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases. In this tech talk, we’ll provide a peek behind the scenes to learn about Amazon ElastiCache's design and architecture. You’ll see common design patterns with our Redis and Memcached offerings and how customers have used them for in-memory operations to reduce latency and improve application throughput. During this session, we review ElastiCache best practices, design patterns, and anti-patterns.
Learning Objectives:
- Learn how to integrate Amazon ElastiCache in your workloads
- Understand the benefits of an In-Memory data store
- Learn how to apply various caching strategies in your applications
- Hands on demonstration using Amazon ElastiCache
AWS re:Invent 2016: Deep Dive: Amazon EMR Best Practices & Design Patterns (B...Amazon Web Services
Amazon EMR is one of the largest Hadoop operators in the world. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost-efficient. Finally, we dive into some of our recent launches to keep you current on our latest features. This session will feature Asurion, a provider of device protection and support services for over 280 million smartphones and other consumer electronics devices. Asurion will share how they architected their petabyte-scale data platform using Apache Hive, Apache Spark, and Presto on Amazon EMR.
Slide-deck used in Bend Web Design and Development Meetup (http://web.archive.org/web/20150728021205/http://www.meetup.com/Bend-Web-Design-and-Development/events/222592014/)
"In this session, you will learn how to easily access your data on S3, and how to visualize and generate insights from Amazon Athena and other data sources through Amazon QuickSight. In addition we will share some tips & best practices for using Athena & QuickSight.
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.
Amazon QuickSight is a fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from various data sources (Amazon Redshift, Amazon Athena, Amazon EMR, Amazon RDS and more)."
Strategic Uses for Cost Efficient Long-Term Cloud StorageAmazon Web Services
Compared to storing long-term datasets on-premises, archiving in the cloud is a smart alternative whether you’re looking for an active archive solution, tape replacement, or to fulfill a compliance requirement. Learn how AWS customers are simplifying their archiving strategy and meeting compliance needs using Amazon Glacier. Hear how customers have evolved their backup and disaster recovery architectures and replaced tape solutions by turning to AWS for a more cost efficient, durable and agile solution. We will showcase Sony DADC's active archive deployment on Glacier and demo how some of our financial service customers have set up compliant archives to meet their regulatory objectives.
(BDT305) Lessons Learned and Best Practices for Running Hadoop on AWS | AWS r...Amazon Web Services
Enterprises are starting to deploy large scale Hadoop clusters to extract value out of the data that they are generating. These clusters often span hundreds of nodes. To speed up the time to value, a lot of the newer deployments are happening in AWS, moving from the traditional on-premises, bare-metal world. Cloudera supports just such deployments. In this session, Cloudera shares the lessons learned and best practices for deploying multi-tenant Hadoop clusters in AWS. They will cover what reference deployments look like, what services are relevant for Hadoop deployments, network configurations, instance types, backup and disaster recovery considerations, and security considerations. They will also talk about what works well, what doesn't, and what has to be done going forward to improve the operability of Hadoop on AWS.
More and more, the scalable on-demand infrastructure provided by AWS is being used by researchers, scientists and engineers in Life Sciences, Finance and Engineering to solve bigger problems, answer complex questions and run larger simulations. In this session we start by talking about the supercomputing class performance and high performance storage available to the scientists and engineers at their fingertips. We will go over examples of how startups are innovating and large enterprises are extending their HPC environments. Finally, we walk through some of the common questions that come up as organizations start leveraging AWS for their high performance computing needs.
Deep Learning for Data Scientists: Using Apache MXNet and R on AWS - June 201...Amazon Web Services
Learning Objectives:
- Deploy a Data science environment in minutes with the AWS -
- Deep Learning AMI
- Getting started with Apache MXNet on R
- Train and deploy Deep Learning models at scale with R
Deep Learning (DL) is a subset of Machine Learning (ML) that extends the concept of Artificial Neural Networks (ANN) to uncover hidden patterns in unstructured datasets. Due to the current ubiquity of data (Big Data), and availability of on-demand, inexpensive, and parallel hardware such as Graphics Processing Units (GPUs) on Amazon EC2, Deep Learning has revitalized the excitement in Artificial Intelligence. Breakthrough results can be seen in industry applications such, computer vision, robotics, healthcare, security, retail, and more. Apache MXNet is a fully-featured, flexibly-programmable and ultra-scalable deep learning framework supporting state-of-the-art deep models including convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). MXNet enables Data Scientists familiar with the R programing language to train and deploy deep models at scale, using their favorite language, with the same fast performance observed by Python, Scala or C++ ML practitioners.
You will also hear from Jared P. Lander, adjunct professor of statistics at Columbia University and the organizer of the New York Open Statistical Programming Meetup—the world’s largest R meetup—and the New York R Conference.
Participants will learn how to spin up a pre-built, GPU enabled Data Science environment using the AWS Deep Learning Amazon Machine Image (AMI), in few minutes. We will write a deep learning program with MXNet in a few lines of codes using the R programming language. We will discuss training deep learning models on one or multiple GPUs via R. Finally, we will compare deep models to some traditional Machine Learning models such as Support Vector Machines or Random Forest.
Visit http:aws.amazon.com/hpc for more information about HPC on AWS.
High Performance Computing (HPC) allows scientists and engineers to solve complex science, engineering, and business problems using applications that require high bandwidth, low latency networking, and very high compute capabilities. AWS allows you to increase the speed of research by running high performance computing in the cloud and to reduce costs by providing Cluster Compute or Cluster GPU servers on-demand without large capital investments. You have access to a full-bisection, high bandwidth network for tightly-coupled, IO-intensive workloads, which enables you to scale out across thousands of cores for throughput-oriented applications.
Tune your Big Data Platform to Work at Scale: Taking Hadoop to the Next Level...Amazon Web Services
Learn how to set up a highly scalable, robust, and secure Hadoop platform using Amazon EMR. We'll perform a demonstration using a 100-node Amazon EMR cluster and take you through the best practices and performance tuning required for different workloads to ensure they are production ready.
Speaker: Amo Abeyaratne, Big Data Consultant, Amazon Web Services
Featured Customer - Ambidata
Consolidate MySQL Shards Into Amazon Aurora Using AWS Database Migration Serv...Amazon Web Services
If you’re running a MySQL database at scale, there’s a good chance you’re sharding your database deployment. Sharding is a useful way to increase the scale of your deployment, but it has drawbacks like higher costs, high administration overheard and lower elasticity. It’s harder to grow or shrink a sharded database deployment to match your traffic patterns. In this session, we will discuss and demonstrate how to use AWS Database Migration Service to consolidate multiple MySQL shards into an Amazon Aurora cluster to reduce cost, improve elasticity and make it easier to manage your database.
Learning Objectives:
Learn how to scale your MySQL database at reduced cost and higher elasticity, by consolidating multiple shards into one Amazon Aurora cluster.
Amazon Elastic MapReduce Deep Dive and Best Practices (BDT404) | AWS re:Inven...Amazon Web Services
Amazon Elastic MapReduce is one of the largest Hadoop operators in the world. Since its launch four years ago, our customers have launched more than 5.5 million Hadoop clusters. In this talk, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
AWS re:Invent 2016: How to Scale and Operate Elasticsearch on AWS (DEV307)Amazon Web Services
Elasticsearch has quickly become the leading open source technology for scaling search and building document services on. Many software providers have come to rely on it to serve the needs of high-performance, production applications.
In this talk, we’ll go deep on lessons learned from three years in production scaling from a few shards to more than 100 spread across 100s of nodes on AWS--to serve real-time queries against 100s of millions of documents.
Attendees will learn:
* How to capacity plan for ES on AWS
* How to scale and reshard on AWS with zero downtime
* What AWS and ES metrics to collect and alert on
* Tips on day to day ES operations
Session sponsored by SignalFx.
(BDT208) A Technical Introduction to Amazon Elastic MapReduceAmazon Web Services
"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way.
In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."
Best Practices for Running Amazon EC2 Spot Instances with Amazon EMR - AWS On...Amazon Web Services
Learning Objectives:
- Learn how to run Amazon EMR clusters on Spot instances and significantly reduce the cost of processing vast amounts of data on managed Hadoop clusters
- Understand key EC2 Spot Instances concepts and common usage patterns for maximum scale and cost optimization for Big Data workloads
- See a few customer examples that show how to leverage the full scale of the AWS cloud for faster results
Those who out-compute can many times out-compete. The cloud gives you access to a massive amount of compute power when you need it. This talk will present an introduction to HPC in the cloud, including, the benefits of HPC in the cloud, how to get started, some tools to use, and how you can manage data. We will showcase several examples of HPC in the cloud by a number of public sector and commercial customers.
Created by: Dr. Jeff Layton, Principal, Solutions Architect
ElastiCache Deep Dive: Best Practices and Usage Patterns - March 2017 AWS Onl...Amazon Web Services
Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases. In this tech talk, we’ll provide a peek behind the scenes to learn about Amazon ElastiCache's design and architecture. You’ll see common design patterns with our Redis and Memcached offerings and how customers have used them for in-memory operations to reduce latency and improve application throughput. During this session, we review ElastiCache best practices, design patterns, and anti-patterns.
Learning Objectives:
- Learn how to integrate Amazon ElastiCache in your workloads
- Understand the benefits of an In-Memory data store
- Learn how to apply various caching strategies in your applications
- Hands on demonstration using Amazon ElastiCache
面對日新月異的大數據工具,有時候很難跟上這節奏。有鑑於此,Amazon Web Services提供了廣泛而完善的雲端運算服務組合,幫助您構建、維護和部署大數據應用程式。
這場線上研討會,將為各位深入淺出介紹AWS 雲端平台提供的各種大數據選項,包括現正流行的大數據框架,如Hadoop、Spark、NoSQL數據庫等,同時透過使用案例來瞭解最佳實踐方式。最後,您將了解如何應用這些工具服務,將大數據導入您的現實應用程式中。
An Overview of Designing Microservices Based Applications on AWS - March 2017...Amazon Web Services
Microservices are an architectural approach to decompose complex applications into smaller, independent services. AWS customers benefit from increased agility, simplified scalability, resiliency, and faster deployments by migrating from monoliths to microservices based architecture.
In this session, we will provide an overview of the benefits and challenges of microservices, and share best practices for architecting and deploying microservices on AWS. We will dive into different approaches you can take to run microservices applications at scale and explore how services like Amazon ECS, AWS Lambda, and AWS X-Ray make it simpler to design and maintain these applications.
Learning Objectives:
1. Understand the fundamentals of the microservices architectural approach
2. Learn best practices for designing microservices on AWS
3. Learn the basics of Amazon EC2 Container Service, AWS Lambda, and AWS X-Ray
Optimizing the Data Tier for Serverless Web Applications - March 2017 Online ...Amazon Web Services
AWS Lambda empowers developers to build cloud-native web applications or platforms using microservices architectures. This tech talk walks you through the process of identifying the presentation, logic, and data tiers required to build web applications with AWS Lambda at the core. By using AWS Lambda as your logic tier, you have a wide number of data storage options for your data tier. AWS offers a wide range of database services to fit your application requirements. We dive into methodologies for picking the right database/datastore technology based on your application requirements. We demonstrate connecting your serverless app to various AWS database offerings including Amazon RDS, Amazon Aurora, Amazon DynamoDB, and Amazon ElastiCache, and elaborate on the setup of each option with AWS Lambda. We also provide guidelines and best practices for implementing this architecture pattern, such as setting up a VPC on Lambda to connect to private resources and managing database connections.
Learning Objectives:
- Understand data-tier options when building serverless applications using AWS Lambda.
- Configuration and connectivity of AWS Lambda with each data tier
- Best practices for database connections and retries
Automating Management of Amazon EC2 Instances with Auto Scaling - March 2017 ...Amazon Web Services
Automation is vital to efficient DevOps, and getting your fleets of EC2 instances to launch, provision software, and self-heal automatically is a key challenge. Auto Scaling provides essential features for each of these instance lifecycle automation steps, which are widely applicable to just about any type of application running on EC2. In this tech talk, you will learn about how to automate launches with Launch Configurations, configure the software environment before your instance accepts traffic using Lifecycle hooks, and how to create a resilient multi-AZ fleet to run your application with minimal effort.
Learning Objectives:
1. Learn how you can improve application availability and operational efficiency by automating fleet L10management for Amazon EC2 instances
2. Understand how Auto Scaling works and how easy it is to control the lifecycle of your fleet and the applications they run
3. Hear about recent developments in the Auto Scaling service how they provide an advantage to a wide variety of applications
One Click Enterprise IoT Services - March 2017 AWS Online Tech TalksAmazon Web Services
The AWS IoT Button is a programmable button based on the Amazon Dash Button hardware offering a one-click experience for users to access applications in the cloud. Enterprises can build fully customized IoT applications, or select from a list of predefined “blueprints” to provide innovative experiences to their consumers, simplify their customer interface, and increase engagement and brand loyalty. In this webinar, we will explain why the AWS IoT Button is the simplest way to get started with IoT and discuss how you can develop applications in the cloud that are activated by one click of the button.
Learning Objectives:
- Learn how to get started with IoT using the AWS IoT Button
- Learn how to leverage the AWS IoT Button to increase customer engagement
- Learn how other AWS customers have used the AWS IoT button to build new experiences
Developing Applications with the IoT Button - March 2017 AWS Online Tech TalksAmazon Web Services
Develop and program the AWS IoT button to create a one-click experience for users to access applications in the cloud. This webinar will show you how you can configure your AWS IoT Button using the AWS IoT Button mobile app. The mobile app simplifies the process of registering, configuring, and programming the button. Using preconfigured AWS Lambda blueprints, the app lets you quickly program the button to send an SMS or email when clicked. Or, you can write your own Lambda code for the functionality of your choice. In this webinar, we will also demo an end to end application using an AWS IoT button.
Learning Objectives:
1. Build a fully customized IoT application
2. Integrate Lambda blueprints for easy functionality
3. see how you can write your own Lambda code
Getting the Most Out of the New Amazon EC2 Reserved Instances Enhancements - ...Amazon Web Services
Several new enhancements to AWS Reserved Instances (including convertible and regional RIs) can allow your organization to save substantially on EC2 spending while maximizing usage flexibility. In this session, we will provide an overview of some of these recent enhancements and explore how your teams can best take advantage of these.
Learning Objectives:
- Understand the new enhancements to Amazon EC2 Reserved Instances
- Learn new ways to unlock cost savings using RIs
Hands-on Labs: Getting Started with AWS - March 2017 AWS Online Tech TalksAmazon Web Services
Millions of customers are leveraging AWS for increased flexibility, scalability, and reliability. Attend this hands-on workshop to learn the basics of AWS as you build a simple static website on AWS. After a brief overview, this session will dive into discussions of core AWS services, such as Amazon S3, Route 53 and Amazon CloudFront and demonstrate how to utilize those services to deploy a static website, associate a domain name for it, and enable it to load quickly. By the end of the hands-on session, you will have your own website running in your AWS account.
Learning Objectives:
• Learn how to deploy a static website using Amazon S3. Amazon S3 will provide the origin for your website as well as storage for your static content.
• Associate your domain name with your website using Amazon Route 53. Amazon Route 53 will tell the Domain Name System (DNS) where to find your website.
• Enable your website to load quickly using Amazon CloudFront. Amazon CloudFront will create a content delivery network (CDN) that hosts your website content in close proximity to your users.
Best Practices for Managing Security Operations in AWS - March 2017 AWS Onlin...Amazon Web Services
To help prevent unexpected access to your AWS resources, it is critical to maintain strong identity and access policies. It is equally important to track and alert on changes to your AWS resources. In this tech talk, you will learn how to use AWS Identity and Access Management (IAM) to control access to your AWS resources and integrate your existing authentication system with AWS IAM. We will cover how you can deploy and control your AWS infrastructure using code templates, including change management policies with AWS CloudFormation. In addition, we will explore different options for managing both your AWS access logs and your Amazon Elastic Compute Cloud (EC2) system logs using Amazon CloudWatch Logs. We also will cover how to use these logs to implement an audit and compliance validation process using services such as AWS Config, AWS CloudTrail, and Amazon Inspector.
Learning Objectives:
• Understand the AWS Shared Responsibility Model.
• Understand AWS account and identity management options and configuration.
• Learn the concept of infrastructure as code and change management using AWS CloudFormation.
• Learn how to audit and log your AWS service usage.
• Learn about AWS services to add automatic compliance checks to your AWS infrastructure.
Deep Dive on Amazon EBS Elastic Volumes - March 2017 AWS Online Tech TalksAmazon Web Services
Amazon Elastic Block Store (Amazon EBS) provides persistent block level storage for use with Amazon EC2 instances. In this technical session, we will present and demonstrate how you can increase capacity, tune performance, and modify volume types on the fly with the latest Amazon EBS innovation, Elastic Volumes. You will learn how Elastic Volumes can significantly reduce both operational complexity and downtime enabling you to right-size your deployment and dynamically adapt as your business needs change. We will describe best practices and share tips for success throughout.
Learning Objectives:
- Learn how to increase capacity, tune performance, and modify volume types
- Learn how you can automate modifications to align with changing business needs.
- Review the different Amazon EBS volume types and receive best practices for each.
Scalable Deep Learning on AWS with Apache MXNetJulien SIMON
Session @ AWS Summit Stockholm, 03/04/2017
AI: The Story So Far
Applications of Deep Learning
Apache MXNet Overview
Apache MXNet API
Code and Demos
Tools and Resources
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. During this workshop, we will provide a short background on Deep Learning focusing on relevant application domains and an introduction to the powerful and scalable Deep Learning framework, Apache MXNet. At the end of this tutorial you’ll be able to train your own deep neural network, fine tune existing state of the art models for image and object recognition. We’ll also deep dive on setting up your deep learning infrastructure on AWS and model deployment on AWS Lambda.
What is Deep Learning
Rise of Deep Learning
Phases of Deep Learning - Training and Inference
AI & Limitations of Deep Learning
Apache MXNet History, Apache MXNet concepts
How to use Apache MXNet and Spark together for Distributed Inference.
by Vikram Madan, Sr. Product Manager, AWS Deep Learning
In this workshop, we will provide cover deep learning fundamentals and focus on the powerful and scalable Apache MXNet open source deep learning framework. At the end of this tutorial you’ll be able to train your own deep neural network and fine tune existing state of the art models for image and object recognition. We’ll also deep dive on setting up your deep learning infrastructure on AWS and model deployment on AWS Lambda.
Machine Learning is increasingly being used by organisations to move from analysis to prediction. How AWS and open source technology can help you to perform both Deep Learning and Machine Learning
Travis Oliphant "Python for Speed, Scale, and Science"Fwdays
Python is sometimes discounted as slow because of its dynamic typing and interpreted nature and not suitable for scale because of the GIL. But, in this talk, I will show how with the help of talented open-source contributors around the world, we have been able to build systems in Python that are fast and scalable to many machines and how this has helped Python take over Science.
Similar to A Deeper Dive into Apache MXNet - March 2017 AWS Online Tech Talks (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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
2. Agenda
• Apache MXNet introduction
• Distributed Deep Learning with AWS Cloudformation
• Deep Learning motivation and basics
• MXNet programing model overview
• Train our first neural network using MXNet
3. Deep Learning Applications
Significantly improve many applications on multiple domains
image understanding speech recognition natural language
processing
autonomy
• Netflix – Recommendation Engine
• FINRA – Anonmaly detection, Sequence matching
• TuSimple - Computer Vision for Autonomous Driving
• Pinterest - Image recognition search
• Mapillary - Computer vision for crowd sourced maps
AI Customers on AWS
4. AI Services
AI Platform
AI Engines
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
More to come
in 2017
Amazon
Machine Learning
Amazon Elastic
MapReduce
Spark &
SparkML
More to come
in 2017
Apache
MXNet
TensorFlow Caffe Theano KerasTorch CNTK
P2 ECS LambdaEMR/Spark GreenGrass FPGA
More to come
in 2017
Hardware
Democratizing Artificial Intelligence
5. Apache MXNet
Programmable Portable High Performance
Near linear scaling
across hundreds of GPUs
Highly efficient
models for mobile
and IoT
Simple syntax,
multiple languages
88% efficiency
on 256 GPUs
Resnet 1024 layer network
is ~4GB
12. Artificial Neuron
output
synaptic
weights
• Input
Vector of training data x
• Output
Linear function of inputs
• Nonlinearity
Transform output into desired range
of values, e.g. for classification we
need probabilities [0, 1]
• Training
Learn the weights w and bias b
13. Deep Neural Network
hidden layers
The optimal size of the hidden
layer (number of neurons) is
usually between the size of the
input and size of the output
layers
Input layer
output
14. The “Learning” in Deep Learning
0.4 0.3
0.2 0.9
...
back propogation (gradient descent)
X1 != X
0.4 ± 𝛿 0.3 ± 𝛿
new
weights
new
weights
0
1
0
1
1
.
.
-
-
X
input
label
...
X1
17. import numpy as np
a = np.ones(10)
b = np.ones(10) * 2
c = b * a
• Straightforward and flexible.
• Take advantage of language
native features (loop,
condition, debugger)
• E.g. Numpy, Matlab, Torch, …
• Hard to optimize
PROS
CONS
d = c + 1c
Easy to tweak
with python codes
Imperative Programing
18. • More chances for optimization
• Cross different languages
• E.g. TensorFlow, Theano,
Caffe
• Less flexible
PROS
CONS
C can share memory with D
because C is deleted later
A = Variable('A')
B = Variable('B')
C = B * A
D = C + 1
f = compile(D)
d = f(A=np.ones(10),
B=np.ones(10)*2)
A B
1
+
X
Declarative Programing
19. IMPERATIVE
NDARRAY API
DECLARATIVE
SYMBOLIC
EXECUTOR
>>> import mxnet as mx
>>> a = mx.nd.zeros((100, 50))
>>> b = mx.nd.ones((100, 50))
>>> c = a + b
>>> c += 1
>>> print(c)
>>> import mxnet as mx
>>> net = mx.symbol.Variable('data')
>>> net = mx.symbol.FullyConnected(data=net, num_hidde
>>> net = mx.symbol.SoftmaxOutput(data=net)
>>> texec = mx.module.Module(net)
>>> texec.forward(data=c)
>>> texec.backward()
NDArray can be set
as input to the graph
MXNet: Mixed programming paradigm
21. MXNet Overview
• Founded by: U.Washington, Carnegie Mellon U. (~1.5yrs old)
• Recently Accepted to the Apache Incubator
• State of the Art Model Support: Convolutional Neural Networks (CNN), Long
Short-Term Memory (LSTM)
• Scalable: Near-linear scaling equals fastest time to model
• Multi-language: Support for Scala, Python, R, etc.. for legacy code leverage and
easy integration with Spark
• Ecosystem: Vibrant community from Academia and Industry
Open Source Project on Github | Apache-2 Licensed
22. Application Examples | Python notebooks
• https://github.com/dmlc/mxnet-notebooks
• Basic concepts
• NDArray - multi-dimensional array computation
• Symbol - symbolic expression for neural networks
• Module - neural network training and inference
• Applications
• MNIST: recognize handwritten digits
• Check out the distributed training results
• Predict with pre-trained models
• LSTMs for sequence learning
• Recommender systems
• Train a state of the art Computer Vision model (CNN)
• Lots more..
23. Call to Action
MXNet Resources:
• MXNet Blog Post | AWS Endorsement
• Read up on MXNet and Learn More: mxnet.io
• MXNet Github Repo
• MXNet Recommender Systems Talk | Leo Dirac
Developer Resources:
• Deep Learning AMI | Amazon Linux
• Deep Learning AMI | Ubuntu – NEW!!!
• P2 Instance Information
• CloudFormation Template Instructions
• Deep Learning Benchmark
• MXNet on Lambda
• MXNet on ECS/Docker
• MXNet on Raspberry Pi | Wine Detector