this series of slides showing a configuration of an Elasticsearch cluster on AWS following the principles of mass production system getting fully reproducible and scalable environments without any handy configuration.
Amazon EC2 is a web service that provides on-demand computing capacity in the cloud. It offers resizable compute capacity that customers can use to launch virtual servers called instances that run on Amazon's computing infrastructure. Customers have control over their instances and can choose from different instance types that vary in CPU, memory, storage, and price. EC2 provides a flexible and reliable computing environment in multiple locations at a low cost.
As the hyper-scale provides now offer GPU-enabled VMS as standard, convergence is arising around containerised AI workloads managed by Kubernetes. This talk demonstrates the approach for combining the power of GPU-enabled VMs in Azure together with Azure Container Service running Kubernetes to build a flexible and scalable platform for AI workloads.
This document discusses using Amazon Elastic MapReduce (EMR) as a cost-effective solution for processing large amounts of data for a startup. It describes how the startup was struggling to manage their growing data on MySQL databases and traditional Hadoop clusters, which were difficult to maintain. Amazon EMR allows companies to launch Hadoop clusters on demand to process data stored in S3 and pay only for the hours used, providing significant cost savings compared to maintaining a permanent cluster. The author details their experience migrating to this approach using EMR and how it helped them overcome their scaling challenges in a simple and cost-efficient manner.
This document discusses using k-means clustering in Spark to detect device anomalies based on device feature data. It provides an example of device data with attributes like battery percentage and RAM usage. It also shows example Scala code to perform k-means clustering on this data, including normalizing the data first before clustering. The results show data points clustered and predictions assigned.
This document discusses the challenges of running Hadoop on AWS and describes the author's personal experience with different approaches. It notes that while AWS can save money, running Hadoop is complex and requires significant tuning. The author details trying Pig/EMR, then Cascalog/EMR, and managing their own Hadoop cluster before ultimately finding the most success with Cascalog/EMR after redesigning their data pipelines to be fault tolerant. Key lessons are to break processing into stages, write to S3 after each, plan for variability, compress S3 data, and "drinking helps."
The document discusses running memcached clusters on Amazon EC2. It covers key concepts like caching, clusters, and infrastructure as a service (AWS). It then provides step-by-step instructions for setting up a memcached cluster on EC2, including creating security groups, launching EC2 instances from AMIs, and configuring the memcached servers and clients. The summary concludes that setting up and running memcached clusters on infrastructure as a service environments like EC2 is straightforward.
This document provides an overview of Apache MXNet, an open-source library for deep learning. It discusses MXNet's capabilities such as high performance scaling across GPUs, support for mobile and IoT models, and multiple language syntax. It also demonstrates MXNet through Jupyter notebooks on MNIST data and introduces Gluon, a high-level API for MXNet. Resources for learning more about MXNet, deep learning on AWS, and the presenter's blog are provided.
Ford's AWS Service Update - March 2020 (Richmond AWS User Group)Ford Prior
1. The document summarizes new and updated AWS services from February 15th to March 5th, 2020 that were presented to an AWS user group.
2. Key updates include EKS availability in new regions, improved EBS-optimized EC2 instances, secrets management integration with ECS, and new IAM condition keys.
3. The summary also briefly outlines updates to several other AWS services including EBS, Lambda, Connect, Transcribe, Greengrass, IoT Core, Control Tower, Bottlerocket, Rekognition, Aurora, and more.
Amazon EC2 is a web service that provides on-demand computing capacity in the cloud. It offers resizable compute capacity that customers can use to launch virtual servers called instances that run on Amazon's computing infrastructure. Customers have control over their instances and can choose from different instance types that vary in CPU, memory, storage, and price. EC2 provides a flexible and reliable computing environment in multiple locations at a low cost.
As the hyper-scale provides now offer GPU-enabled VMS as standard, convergence is arising around containerised AI workloads managed by Kubernetes. This talk demonstrates the approach for combining the power of GPU-enabled VMs in Azure together with Azure Container Service running Kubernetes to build a flexible and scalable platform for AI workloads.
This document discusses using Amazon Elastic MapReduce (EMR) as a cost-effective solution for processing large amounts of data for a startup. It describes how the startup was struggling to manage their growing data on MySQL databases and traditional Hadoop clusters, which were difficult to maintain. Amazon EMR allows companies to launch Hadoop clusters on demand to process data stored in S3 and pay only for the hours used, providing significant cost savings compared to maintaining a permanent cluster. The author details their experience migrating to this approach using EMR and how it helped them overcome their scaling challenges in a simple and cost-efficient manner.
This document discusses using k-means clustering in Spark to detect device anomalies based on device feature data. It provides an example of device data with attributes like battery percentage and RAM usage. It also shows example Scala code to perform k-means clustering on this data, including normalizing the data first before clustering. The results show data points clustered and predictions assigned.
This document discusses the challenges of running Hadoop on AWS and describes the author's personal experience with different approaches. It notes that while AWS can save money, running Hadoop is complex and requires significant tuning. The author details trying Pig/EMR, then Cascalog/EMR, and managing their own Hadoop cluster before ultimately finding the most success with Cascalog/EMR after redesigning their data pipelines to be fault tolerant. Key lessons are to break processing into stages, write to S3 after each, plan for variability, compress S3 data, and "drinking helps."
The document discusses running memcached clusters on Amazon EC2. It covers key concepts like caching, clusters, and infrastructure as a service (AWS). It then provides step-by-step instructions for setting up a memcached cluster on EC2, including creating security groups, launching EC2 instances from AMIs, and configuring the memcached servers and clients. The summary concludes that setting up and running memcached clusters on infrastructure as a service environments like EC2 is straightforward.
This document provides an overview of Apache MXNet, an open-source library for deep learning. It discusses MXNet's capabilities such as high performance scaling across GPUs, support for mobile and IoT models, and multiple language syntax. It also demonstrates MXNet through Jupyter notebooks on MNIST data and introduces Gluon, a high-level API for MXNet. Resources for learning more about MXNet, deep learning on AWS, and the presenter's blog are provided.
Ford's AWS Service Update - March 2020 (Richmond AWS User Group)Ford Prior
1. The document summarizes new and updated AWS services from February 15th to March 5th, 2020 that were presented to an AWS user group.
2. Key updates include EKS availability in new regions, improved EBS-optimized EC2 instances, secrets management integration with ECS, and new IAM condition keys.
3. The summary also briefly outlines updates to several other AWS services including EBS, Lambda, Connect, Transcribe, Greengrass, IoT Core, Control Tower, Bottlerocket, Rekognition, Aurora, and more.
Logging with Elasticsearch, Logstash & KibanaAmazee Labs
This document discusses logging with the ELK stack (Elasticsearch, Logstash, Kibana). It provides an overview of each component, how they work together, and demos their use. Elasticsearch is for search and indexing, Logstash centralizes and parses logs, and Kibana provides visualization. Tools like Curator help manage time-series data in Elasticsearch. The speaker demonstrates collecting syslog data with Logstash and viewing it in Kibana. The ELK stack provides centralized logging and makes queries like "check errors from yesterday between times" much easier.
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
Flink vs. Spark: this is the slide deck of my talk at the 2015 Flink Forward conference in Berlin, Germany, on October 12, 2015. In this talk, we tried to compare Apache Flink vs. Apache Spark with focus on real-time stream processing. Your feedback and comments are much appreciated.
Attack monitoring using ElasticSearch Logstash and KibanaPrajal Kulkarni
This document discusses using the ELK stack (Elasticsearch, Logstash, Kibana) for attack monitoring. It provides an overview of each component, describes how to set up ELK and configure Logstash for log collection and parsing. It also demonstrates log forwarding using Logstash Forwarder, and shows how to configure alerts and dashboards in Kibana for attack monitoring. Examples are given for parsing Apache logs and syslog using Grok filters in Logstash.
Weaveworks at AWS re:Invent 2016: Operations Management with Amazon ECSWeaveworks
Alfonso described how Weave open source projects (Weave Net and Weave Scope) can help with networking, visualization, and control for ECS. Specifically, Weave acts as a key communicator for networking containers with its multi-host overlay and additional features (including automatic DNS service discovery and multicast).
Julien Simon "Scaling ML from 0 to millions of users"Fwdays
This document discusses scaling machine learning models from a single instance to millions of users. It begins by describing starting with a model on a local machine and then deploying it on a single EC2 instance. It notes the issues that arise with this approach as needs increase. It then discusses options for scaling to multiple instances, Docker clusters using ECS/EKS, and the fully managed SageMaker service. SageMaker is argued to require minimal effort for infrastructure and deployment compared to the other options as it scales models easily and focuses solely on machine learning tasks.
Ansible ans Amazon AWS services can cooperate nicely, here are the slides I used for talk: https://www.youtube.com/watch?list=PLub6xBWO8gV_Mr-UuxrHcfUbuGv5n_N5g&v=vPes2x5ToUk
SRV402 Deep Dive on Amazon EC2 Instances, Featuring Performance Optimization ...Amazon Web Services
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and Accelerated Computing (GPU and FPGA) instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Scaling drupal horizontally and in cloudVladimir Ilic
Vancouver Drupal group presentation for April 25, 2013.
How to deploy Drupal on
- multiple web servers,
- multiple web and database servers, and
- how to join all that together and make site deployed on Amazon Cloud (Virtual Private Cloud) inside
- one availability zone
- multiple availability zones deployment.
Session cover details about what you need in order to get Drupal deployed on separate servers, what are issues/concerns, and how to solve them.
講師: Jhen-Wei Huang, Solution Architect, AWS
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 reinvent 2019 recap - Riyadh - Containers and Serverless - Paul MaddoxAWS Riyadh User Group
This document provides an overview and agenda for an AWS storage, compute, containers, serverless, and management tools presentation. It includes summaries of several upcoming AWS services and features related to EBS, S3, EC2, EKS, Fargate, Lambda, and AWS Cost Optimizer. The speaker is introduced as Paul Maddox, Principal Architect at AWS, with a background in development, SRE, and systems architecture.
Aran Khanna, Software Engineer, Amazon Web Services at MLconf ATL 2017MLconf
High Performance Deep Learning on Edge Devices With Apache MXNet:
Deep network based models are marked by an asymmetry between the large amount of compute power needed to train a model, and the relatively small amount of compute power needed to deploy a trained model for inference. This is particularly true in computer vision tasks such as object detection or image classification, where millions of labeled images and large numbers of GPUs are needed to produce an accurate model that can be deployed for inference on low powered devices with a single CPU. The challenge when deploying vision models on these low powered devices though, is getting inference to run efficiently enough to allow for near real time processing of a video stream. Fortunately Apache MXNet provides the tools to solve this issues, allowing users to create highly performant models with tools like separable convolutions, quantized weights and sparsity exploitation as well as providing custom hardware kernels to ensure inference calculations are accelerated to the maximum amount allowed by the hardware the model is being deployed on. This is demonstrated though a state of the art MXNet based vision network running in near real time on a low powered Raspberry Pi device. We finally discuss how running inference at the edge as well as leveraging MXNet’s efficient modeling tools can be used to massively drive down compute costs for deploying deep networks in a production system at scale.
SRV402 Deep Dive on Amazon EC2 Instances, Featuring Performance Optimization ...Amazon Web Services
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and Accelerated Computing (GPU and FPGA) instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Deep Dive on Amazon EC2 Instances (March 2017)Julien SIMON
This document provides an overview of Amazon EC2 instance types and performance optimization best practices. It discusses the factors that go into choosing an EC2 instance, how instance performance is characterized, and how to optimize workloads through choices like instance type, operating system, and configuration settings. Specific tips are provided around topics like timekeeping, CPU credit monitoring, NUMA, and kernel optimizations. The goal is to help users make the most of their EC2 experience through understanding instance internals and performance tradeoffs.
The document provides an overview of Amazon EC2 instance types and best practices for optimizing performance. It discusses factors to consider when choosing an EC2 instance, how instances deliver performance and flexibility, and tips for making the most of different instance types. The document reviews EC2 instance history, describes virtual CPUs and resource allocation, and provides guidance on topics like NUMA, hugepages, operating systems, and hardware aspects that impact performance.
The document provides an overview of Amazon EC2, including:
1. It discusses Amazon EC2 concepts such as regions, availability zones, and instance types.
2. It covers data storage options for EC2 including instance store, EBS volumes, snapshots, and encryption.
3. It discusses EC2 networking components like VPC, subnets, security groups, load balancers and peering.
4. It provides an introduction to monitoring EC2 with CloudWatch, including metrics, logs and alarms.
5. It touches on security and access control using IAM roles and key pairs.
6. It outlines different deployment strategies like baking AMIs, configuring dynamically, and using auto scaling
- Ideato uses Ansible to provision and configure 50+ VMs across development, staging, and production environments. This allows developers easy configuration of their environments and saves sysadmins time on maintenance tasks.
- Ansible roles provide a painless way to perform rolling updates across environments similarly to Puppet modules. Learning YAML is easier than Ruby DSLs for configuring nodes.
- A demo was shown using Ansible to deploy an Elasticsearch cluster on AWS across multiple availability zones for high availability. Tasks included launching EC2 instances, configuring the cluster, and inserting sample data.
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.
This document discusses tools for AI development including Visual Studio Code, the Data Science Virtual Machine (DSVM), and Azure Batch AI. It provides overviews and links to resources for setting up environments for AI/ML development and training models at scale using these tools on Azure. Key points covered include Visual Studio Code extensions for AI, the DSVM for local development, Azure Batch AI for distributed training at scale, and tools like aztk and Spark on DSVM for end-to-end model development.
Logging with Elasticsearch, Logstash & KibanaAmazee Labs
This document discusses logging with the ELK stack (Elasticsearch, Logstash, Kibana). It provides an overview of each component, how they work together, and demos their use. Elasticsearch is for search and indexing, Logstash centralizes and parses logs, and Kibana provides visualization. Tools like Curator help manage time-series data in Elasticsearch. The speaker demonstrates collecting syslog data with Logstash and viewing it in Kibana. The ELK stack provides centralized logging and makes queries like "check errors from yesterday between times" much easier.
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
Flink vs. Spark: this is the slide deck of my talk at the 2015 Flink Forward conference in Berlin, Germany, on October 12, 2015. In this talk, we tried to compare Apache Flink vs. Apache Spark with focus on real-time stream processing. Your feedback and comments are much appreciated.
Attack monitoring using ElasticSearch Logstash and KibanaPrajal Kulkarni
This document discusses using the ELK stack (Elasticsearch, Logstash, Kibana) for attack monitoring. It provides an overview of each component, describes how to set up ELK and configure Logstash for log collection and parsing. It also demonstrates log forwarding using Logstash Forwarder, and shows how to configure alerts and dashboards in Kibana for attack monitoring. Examples are given for parsing Apache logs and syslog using Grok filters in Logstash.
Weaveworks at AWS re:Invent 2016: Operations Management with Amazon ECSWeaveworks
Alfonso described how Weave open source projects (Weave Net and Weave Scope) can help with networking, visualization, and control for ECS. Specifically, Weave acts as a key communicator for networking containers with its multi-host overlay and additional features (including automatic DNS service discovery and multicast).
Julien Simon "Scaling ML from 0 to millions of users"Fwdays
This document discusses scaling machine learning models from a single instance to millions of users. It begins by describing starting with a model on a local machine and then deploying it on a single EC2 instance. It notes the issues that arise with this approach as needs increase. It then discusses options for scaling to multiple instances, Docker clusters using ECS/EKS, and the fully managed SageMaker service. SageMaker is argued to require minimal effort for infrastructure and deployment compared to the other options as it scales models easily and focuses solely on machine learning tasks.
Ansible ans Amazon AWS services can cooperate nicely, here are the slides I used for talk: https://www.youtube.com/watch?list=PLub6xBWO8gV_Mr-UuxrHcfUbuGv5n_N5g&v=vPes2x5ToUk
SRV402 Deep Dive on Amazon EC2 Instances, Featuring Performance Optimization ...Amazon Web Services
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and Accelerated Computing (GPU and FPGA) instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Scaling drupal horizontally and in cloudVladimir Ilic
Vancouver Drupal group presentation for April 25, 2013.
How to deploy Drupal on
- multiple web servers,
- multiple web and database servers, and
- how to join all that together and make site deployed on Amazon Cloud (Virtual Private Cloud) inside
- one availability zone
- multiple availability zones deployment.
Session cover details about what you need in order to get Drupal deployed on separate servers, what are issues/concerns, and how to solve them.
講師: Jhen-Wei Huang, Solution Architect, AWS
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 reinvent 2019 recap - Riyadh - Containers and Serverless - Paul MaddoxAWS Riyadh User Group
This document provides an overview and agenda for an AWS storage, compute, containers, serverless, and management tools presentation. It includes summaries of several upcoming AWS services and features related to EBS, S3, EC2, EKS, Fargate, Lambda, and AWS Cost Optimizer. The speaker is introduced as Paul Maddox, Principal Architect at AWS, with a background in development, SRE, and systems architecture.
Aran Khanna, Software Engineer, Amazon Web Services at MLconf ATL 2017MLconf
High Performance Deep Learning on Edge Devices With Apache MXNet:
Deep network based models are marked by an asymmetry between the large amount of compute power needed to train a model, and the relatively small amount of compute power needed to deploy a trained model for inference. This is particularly true in computer vision tasks such as object detection or image classification, where millions of labeled images and large numbers of GPUs are needed to produce an accurate model that can be deployed for inference on low powered devices with a single CPU. The challenge when deploying vision models on these low powered devices though, is getting inference to run efficiently enough to allow for near real time processing of a video stream. Fortunately Apache MXNet provides the tools to solve this issues, allowing users to create highly performant models with tools like separable convolutions, quantized weights and sparsity exploitation as well as providing custom hardware kernels to ensure inference calculations are accelerated to the maximum amount allowed by the hardware the model is being deployed on. This is demonstrated though a state of the art MXNet based vision network running in near real time on a low powered Raspberry Pi device. We finally discuss how running inference at the edge as well as leveraging MXNet’s efficient modeling tools can be used to massively drive down compute costs for deploying deep networks in a production system at scale.
SRV402 Deep Dive on Amazon EC2 Instances, Featuring Performance Optimization ...Amazon Web Services
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and Accelerated Computing (GPU and FPGA) instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Deep Dive on Amazon EC2 Instances (March 2017)Julien SIMON
This document provides an overview of Amazon EC2 instance types and performance optimization best practices. It discusses the factors that go into choosing an EC2 instance, how instance performance is characterized, and how to optimize workloads through choices like instance type, operating system, and configuration settings. Specific tips are provided around topics like timekeeping, CPU credit monitoring, NUMA, and kernel optimizations. The goal is to help users make the most of their EC2 experience through understanding instance internals and performance tradeoffs.
The document provides an overview of Amazon EC2 instance types and best practices for optimizing performance. It discusses factors to consider when choosing an EC2 instance, how instances deliver performance and flexibility, and tips for making the most of different instance types. The document reviews EC2 instance history, describes virtual CPUs and resource allocation, and provides guidance on topics like NUMA, hugepages, operating systems, and hardware aspects that impact performance.
The document provides an overview of Amazon EC2, including:
1. It discusses Amazon EC2 concepts such as regions, availability zones, and instance types.
2. It covers data storage options for EC2 including instance store, EBS volumes, snapshots, and encryption.
3. It discusses EC2 networking components like VPC, subnets, security groups, load balancers and peering.
4. It provides an introduction to monitoring EC2 with CloudWatch, including metrics, logs and alarms.
5. It touches on security and access control using IAM roles and key pairs.
6. It outlines different deployment strategies like baking AMIs, configuring dynamically, and using auto scaling
- Ideato uses Ansible to provision and configure 50+ VMs across development, staging, and production environments. This allows developers easy configuration of their environments and saves sysadmins time on maintenance tasks.
- Ansible roles provide a painless way to perform rolling updates across environments similarly to Puppet modules. Learning YAML is easier than Ruby DSLs for configuring nodes.
- A demo was shown using Ansible to deploy an Elasticsearch cluster on AWS across multiple availability zones for high availability. Tasks included launching EC2 instances, configuring the cluster, and inserting sample data.
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.
This document discusses tools for AI development including Visual Studio Code, the Data Science Virtual Machine (DSVM), and Azure Batch AI. It provides overviews and links to resources for setting up environments for AI/ML development and training models at scale using these tools on Azure. Key points covered include Visual Studio Code extensions for AI, the DSVM for local development, Azure Batch AI for distributed training at scale, and tools like aztk and Spark on DSVM for end-to-end model development.
How to build a Citrix infrastructure on AWSDenis Gundarev
This document summarizes Denis Gundarev's presentation on how to build a Citrix infrastructure in the Amazon Web Services (AWS) cloud. The presentation covered:
- An overview of AWS services like EC2, S3, VPC, RDS, and how to monitor with CloudWatch
- Common Citrix deployment architectures on AWS like using NetScaler and AutoScaling
- Limitations of running Citrix on AWS like lack of capacity management and client OS support
- Guidelines for deploying Citrix on AWS like starting simple, proper sizing, and careful VPC planning
1. The document demonstrates how to use various AWS services like Kinesis, Redshift, Elasticsearch to analyze streaming game log data.
2. It shows setting up an EC2 instance to generate logs, creating a Kinesis stream to ingest the logs, and building Redshift tables to run queries on the logs.
3. The document also explores loading the logs from Kinesis into Elasticsearch for search and linking Kinesis and Redshift with Kinesis Analytics for real-time SQL queries on streams.
"Scaling ML from 0 to millions of users", Julien Simon, AWS Dev Day Kyiv 2019Provectus
AWS Dev Day Kyiv 2019
Track: Analytics & Machine Learning
Session: ""Scaling ML from 0 to millions of users""
Speaker: Julien Simon, Global AI & Machine Learning Evangelist at AWS
Level: 300
AWS Dev Day is a free, full-day technical event where new developers will learn about some of the hottest topics in cloud computing, and experienced developers can dive deep on newer AWS services.
Provectus has organized AWS Dev Day Kyiv in close collaboration with Amazon Web Services: 800+ participants, 18 sessions, 3 tracks, a really AWSome Day!
Now, together with Zeo Alliance, we're building and nurturing AWS User Group Ukraine — join us on Facebook to stay updated about cloud technologies and AWS services: https://www.facebook.com/groups/AWSUserGroupUkraine
Video: https://www.youtube.com/watch?v=N73u1mx9DqY
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key features, and the concept of instance generations.
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
What to do when you have a perfect model for your software but you are constrained by an imperfect business model?
This talk explores the challenges of bringing modelling rigour to the business and strategy levels, and talking to your non-technical counterparts in the process.
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
Project Management: The Role of Project Dashboards.pdfKarya Keeper
Project management is a crucial aspect of any organization, ensuring that projects are completed efficiently and effectively. One of the key tools used in project management is the project dashboard, which provides a comprehensive view of project progress and performance. In this article, we will explore the role of project dashboards in project management, highlighting their key features and benefits.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
Consistent toolbox talks are critical for maintaining workplace safety, as they provide regular opportunities to address specific hazards and reinforce safe practices.
These brief, focused sessions ensure that safety is a continual conversation rather than a one-time event, which helps keep safety protocols fresh in employees' minds. Studies have shown that shorter, more frequent training sessions are more effective for retention and behavior change compared to longer, infrequent sessions.
Engaging workers regularly, toolbox talks promote a culture of safety, empower employees to voice concerns, and ultimately reduce the likelihood of accidents and injuries on site.
The traditional method of conducting safety talks with paper documents and lengthy meetings is not only time-consuming but also less effective. Manual tracking of attendance and compliance is prone to errors and inconsistencies, leading to gaps in safety communication and potential non-compliance with OSHA regulations. Switching to a digital solution like Safelyio offers significant advantages.
Safelyio automates the delivery and documentation of safety talks, ensuring consistency and accessibility. The microlearning approach breaks down complex safety protocols into manageable, bite-sized pieces, making it easier for employees to absorb and retain information.
This method minimizes disruptions to work schedules, eliminates the hassle of paperwork, and ensures that all safety communications are tracked and recorded accurately. Ultimately, using a digital platform like Safelyio enhances engagement, compliance, and overall safety performance on site. https://safelyio.com/
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...XfilesPro
Wondering how X-Sign gained popularity in a quick time span? This eSign functionality of XfilesPro DocuPrime has many advancements to offer for Salesforce users. Explore them now!
Preparing Non - Technical Founders for Engaging a Tech AgencyISH Technologies
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3. Why Elasticsearch is fit for CM management
tools like Ansible?
Lot of sys adm configuration tips for a cluster
environment
• java settings( jmx, mlockall….)
• sysctl settings( swappiness, max_map,count..)
• ulimit settings
Do I have to change these settings by hand
repeated for n° instance times?
NOTHANKS!
4. As a mention before Ansible has a plenty of
sysadm modules:
- name: firewalld applying conf
firewalld: service=elasticsearch
permanent=true zone=public state=enabled
tags:
- firewall
- name: sysctl configs
sysctl: name=fs.file-max value=64000 state=present
tags:
- sysctl
5. Here’ s come AWS
AWS provides a special plugin for discovery your ES
instances inside your cluster just by
their security group!
discovery.type: ec2
discovery.zen.ping.multicast.enabled: false
discovery.ec2.groups: my_security_group
I don’t have to update the other node -1 configurations
if i need to replace or add a new node!!
12. What we have achieved?
• a mass production system without handy configuration
• a fully reproducible environment
• scalability
• availability
• exit staff proof
• fully documentated by the code
• reduced stress
……………………………………………………