5 ways you can build cost-awareness into your cloud architectures and maximize your savings (business-driven auto scaling, mixing and matching reserved/on-demand, iterating and optimizing fungible resources, follow the customer (run auto scaling web servers) during the day and follow the money (run hadoop and transcoding jobs) at night and soak up your reservations.
This Presentation was prepared by Abdussamad Muntahi for the Seminar on High Performance Computing on 11/7/13 (Thursday) Organized by BRAC University Computer Club (BUCC) in collaboration with BRAC University Electronics and Electrical Club (BUEEC).
An overview of some key concepts of chatbots, with some do's and don'ts.
We will happily present the high-resolution version of this presentation, extended with additional detailed slides, and a clear explanation at your offices. Contact us for that.
A (Fairly) Complete Guide to Performance Budgets [SmashingConf SF 2023]Tammy Everts
It's easier to make a fast website than it is to keep a website fast. If you've invested countless hours in speeding up your pages, but you're not using performance budgets to prevent regressions, you could be at risk of wasting all your efforts.
In this talk delivered at Smashing Conference SF in 2023, , we'll cover how to:
• Understand the difference between performance budgets and performance goals
• Identify which metrics to track
• Validate your metrics to make sure they're measuring what you think they are – and to see how they correlate with your user experience and business metrics
• Determine your budget thresholds
• Get buy-in from different stakeholders in your organization
• Integrate with your CI/CD process
• Maintain your budgets so you stay fast!
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...Simplilearn
This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
This is Apache Pig & Pig Latin Session.
We provide training on Big Data & Hadoop,Hadoop Admin ,MongoDB,Data Analytics with R, Python..etc
Our Big Data & Hadoop course consists of Introduction of Hadoop and Big Data,HDFS architecture ,MapReduce ,YARN ,PIG Latin ,Hive,HBase,Mahout,Zookeeper,Oozie,Flume,Spark,Nosql with quizzes and assignments.
To watch the video or know more about the course, please visit http://www.knowbigdata.com/page/big-data-and-hadoop-online-instructor-led-training
The Ultimate Guide to Implementing Conversational AICeline Rayner
What exactly is conversational AI? How is it different than chatbots? How does it work, and why should you implement it?
In the most comprehensive guide ever written on this topic, we cover every single facet of successful, pain-free conversational AI implementation and maintenance in 2021.
This Presentation was prepared by Abdussamad Muntahi for the Seminar on High Performance Computing on 11/7/13 (Thursday) Organized by BRAC University Computer Club (BUCC) in collaboration with BRAC University Electronics and Electrical Club (BUEEC).
An overview of some key concepts of chatbots, with some do's and don'ts.
We will happily present the high-resolution version of this presentation, extended with additional detailed slides, and a clear explanation at your offices. Contact us for that.
A (Fairly) Complete Guide to Performance Budgets [SmashingConf SF 2023]Tammy Everts
It's easier to make a fast website than it is to keep a website fast. If you've invested countless hours in speeding up your pages, but you're not using performance budgets to prevent regressions, you could be at risk of wasting all your efforts.
In this talk delivered at Smashing Conference SF in 2023, , we'll cover how to:
• Understand the difference between performance budgets and performance goals
• Identify which metrics to track
• Validate your metrics to make sure they're measuring what you think they are – and to see how they correlate with your user experience and business metrics
• Determine your budget thresholds
• Get buy-in from different stakeholders in your organization
• Integrate with your CI/CD process
• Maintain your budgets so you stay fast!
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...Simplilearn
This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
This is Apache Pig & Pig Latin Session.
We provide training on Big Data & Hadoop,Hadoop Admin ,MongoDB,Data Analytics with R, Python..etc
Our Big Data & Hadoop course consists of Introduction of Hadoop and Big Data,HDFS architecture ,MapReduce ,YARN ,PIG Latin ,Hive,HBase,Mahout,Zookeeper,Oozie,Flume,Spark,Nosql with quizzes and assignments.
To watch the video or know more about the course, please visit http://www.knowbigdata.com/page/big-data-and-hadoop-online-instructor-led-training
The Ultimate Guide to Implementing Conversational AICeline Rayner
What exactly is conversational AI? How is it different than chatbots? How does it work, and why should you implement it?
In the most comprehensive guide ever written on this topic, we cover every single facet of successful, pain-free conversational AI implementation and maintenance in 2021.
Building a multi headed model thats capable of detecting different types of toxicity like threats, obscenity, insult and identity based hate. Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to efficiently facilitate conversations, leading many communities to limit or completely shut down user comments. So far we have a range of publicly available models served through the perspective APIs, including toxicity. But the current models still make errors, and they dont allow users to select which type of toxicity theyre interested in finding. Pallam Ravi | Hari Narayana Batta | Greeshma S | Shaik Yaseen ""Toxic Comment Classification"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23464.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/23464/toxic-comment-classification/pallam-ravi
How to use kakfa for storing intermediate data and use it as a pub/sub model with each of the Producer/Consumer/Topic configs deeply and the Internals working of it.
Introduction to Pig | Pig Architecture | Pig FundamentalsSkillspeed
This Hadoop Pig tutorial will unravel Pig Programming, Pig Commands, Pig Fundamentals, Grunt Mode, Script Mode & Embedded Mode.
At the end, you'll have a strong knowledge regarding Hadoop Pig Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is Pig?
✓ Pig Data Flows
✓ Pig Programming
----------
What is Pig?
Pig is an open source data flow language which processes data management operations via simple scripts using Pig Latin. Pig works very closely in relation with MapReduce.
----------
Applications of Pig
1. Data Cleansing
2. Data Transfers via HDFS
3. Data Factory Operations
4. Predictive Modelling
5. Business Intelligence
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
Chat bot making process using Python 3 & TensorFlowJeongkyu Shin
Recently, chat bot has become the center of public attention as a new mobile user interface since 2015. Chat bots are widely used to reduce human-to-human interaction, from consultation to online shopping and negotiation, and still expanding the application coverage. Also, chat bot is the basic of conversational interface and non-physical input interface with combination of voice recognition.
Traditional chat bots were developed based on the natural language processing (NLP) and bayesian statistics for user intention recognition and template-based response. However, since 2012, accelerated advance in deep-learning technology and NLPs using deep-learning opened the possibilities to create chat bots with machine learning. Machine learning (ML)-based chat bot development has advantages, for instance, ML-based bots can generate (somewhat non-sense but acceptable) responses to random asks that has no connection with the context once the model is constructed with appropriate learning level.
In this talk, I will introduce the garage chat bot creation process step-by-step. I share the idea and implementations of multi-modal machine learning model with context engine and conversion engine. Also, how to implement Korean natural language processing, continuous conversion and tone manipulation is also discussed.
Chat bot (챗 봇)은 2015년부터 모바일을 중심으로 새로운 사용자 UI로 주목받고 있다. 챗 봇은 상담시 인간-인간 인터랙션을 줄이는 용도부터 온라인 쇼핑 구매에 이르기까지 다양한 분야에 활용되고 있으며 그 범위를 넓혀 나가고 있다. 챗 봇은 대화형 인터페이스의 기초이면서 동시에 (음성 인식과 결합을 통한) 무입력 방식 인터페이스의 기반 기술이기도 하다.
기존의 챗 봇들은 자연어 분석과 베이지안 통계에 기반한 사용자 의도 패턴 인식과 그에 따른 템플릿 응답을 기본 원리로 하여 개발되었다. 그러나 2012년 이후 급속도로 발전한 딥러닝 및 그에 기초한 자연어 인식 기술은 기계 학습을 이용해 챗 봇을 만들 수 있는 가능성을 열었다. 기계학습을 통해 챗 봇을 개발할 경우, 충분한 학습도의 모델을 구축한 후에는 학습 데이터에 따라 컨텍스트에서 벗어난 임의의 문장 입력에 대해서도 적당한 답을 생성할 수 있다는 장점이 있다.
이 발표에서는 Python 3 및 TensorFlow를 이용하여 딥러닝 기반의 챗 봇을 만들 경우에 경험하게 되는 문제점들 및 해결 방법을 다룬다. 봇의 컨텍스트 엔진과 대화 엔진간의 다형성 모델을 구현하고 연결하는 아이디어와 함께 자연어 처리 및 연속 대화 구현, 어법 처리 등을 어떻게 모델링할 수 있는 지에 대한 아이디어 및 구현과 팁을 공유하고자 한다.
This book is about new technology of Openio's ChatGPT.
Published By:
http://braddicksholidaycentre.co.uk/
Braddicks Holiday Centre is a holiday park located in Westward Ho North Devon offering family holidays and pet friendly accommodation next to the seaside.
Interested in building real-time apps like gmail or facebook? In many cases we need to notify clients immediately when something happens: stock prices, online games, chats, betting apps, etc. In this talk we will discuss how to design and build data-streaming API using WebSockets and other related technologies. We plan to cover problems and challenges and overview several libraries and products in this area.
Ant Colony Optimization for Load Balancing in CloudChanda Korat
here the presentation gives the natural behavior of ants and how the that logic is applicable to cloud for load balancing is discussed here with detailed literature survey.
Distributed data-intensive systems are increasingly designed to be only eventually consistent. Persistent data is no longer processed with serialized and transactional access, exposing applications to a range of potential consistency and concurrency anomalies that need to be handled by the application itself. Controlling concurrent data access in monolithic systems is already challenging, but the problem is exacerbated in distributed systems. To make it worse, only little systematic engineering guidance is provided by the software architecture community regarding this issue.
Susanne shares her experiences from different case studies with industry clients, and novel design guidelines developed by using action research. You will learn settled and novel approaches to tackle consistency- and concurrency related design challenges.
Cluster computing is a type of computing where a group of several computers are linked together, allowing the entire group of computers to behave as if it were a single entity. There are a wide variety of different reasons why people might use cluster computing for various computer tasks. It s also used to make sure that a computing system will always be available. It is unknown when this cluster computing concept was first developed, and several different organizations have claimed to have invented it.
Building a multi headed model thats capable of detecting different types of toxicity like threats, obscenity, insult and identity based hate. Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to efficiently facilitate conversations, leading many communities to limit or completely shut down user comments. So far we have a range of publicly available models served through the perspective APIs, including toxicity. But the current models still make errors, and they dont allow users to select which type of toxicity theyre interested in finding. Pallam Ravi | Hari Narayana Batta | Greeshma S | Shaik Yaseen ""Toxic Comment Classification"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23464.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/23464/toxic-comment-classification/pallam-ravi
How to use kakfa for storing intermediate data and use it as a pub/sub model with each of the Producer/Consumer/Topic configs deeply and the Internals working of it.
Introduction to Pig | Pig Architecture | Pig FundamentalsSkillspeed
This Hadoop Pig tutorial will unravel Pig Programming, Pig Commands, Pig Fundamentals, Grunt Mode, Script Mode & Embedded Mode.
At the end, you'll have a strong knowledge regarding Hadoop Pig Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is Pig?
✓ Pig Data Flows
✓ Pig Programming
----------
What is Pig?
Pig is an open source data flow language which processes data management operations via simple scripts using Pig Latin. Pig works very closely in relation with MapReduce.
----------
Applications of Pig
1. Data Cleansing
2. Data Transfers via HDFS
3. Data Factory Operations
4. Predictive Modelling
5. Business Intelligence
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
Chat bot making process using Python 3 & TensorFlowJeongkyu Shin
Recently, chat bot has become the center of public attention as a new mobile user interface since 2015. Chat bots are widely used to reduce human-to-human interaction, from consultation to online shopping and negotiation, and still expanding the application coverage. Also, chat bot is the basic of conversational interface and non-physical input interface with combination of voice recognition.
Traditional chat bots were developed based on the natural language processing (NLP) and bayesian statistics for user intention recognition and template-based response. However, since 2012, accelerated advance in deep-learning technology and NLPs using deep-learning opened the possibilities to create chat bots with machine learning. Machine learning (ML)-based chat bot development has advantages, for instance, ML-based bots can generate (somewhat non-sense but acceptable) responses to random asks that has no connection with the context once the model is constructed with appropriate learning level.
In this talk, I will introduce the garage chat bot creation process step-by-step. I share the idea and implementations of multi-modal machine learning model with context engine and conversion engine. Also, how to implement Korean natural language processing, continuous conversion and tone manipulation is also discussed.
Chat bot (챗 봇)은 2015년부터 모바일을 중심으로 새로운 사용자 UI로 주목받고 있다. 챗 봇은 상담시 인간-인간 인터랙션을 줄이는 용도부터 온라인 쇼핑 구매에 이르기까지 다양한 분야에 활용되고 있으며 그 범위를 넓혀 나가고 있다. 챗 봇은 대화형 인터페이스의 기초이면서 동시에 (음성 인식과 결합을 통한) 무입력 방식 인터페이스의 기반 기술이기도 하다.
기존의 챗 봇들은 자연어 분석과 베이지안 통계에 기반한 사용자 의도 패턴 인식과 그에 따른 템플릿 응답을 기본 원리로 하여 개발되었다. 그러나 2012년 이후 급속도로 발전한 딥러닝 및 그에 기초한 자연어 인식 기술은 기계 학습을 이용해 챗 봇을 만들 수 있는 가능성을 열었다. 기계학습을 통해 챗 봇을 개발할 경우, 충분한 학습도의 모델을 구축한 후에는 학습 데이터에 따라 컨텍스트에서 벗어난 임의의 문장 입력에 대해서도 적당한 답을 생성할 수 있다는 장점이 있다.
이 발표에서는 Python 3 및 TensorFlow를 이용하여 딥러닝 기반의 챗 봇을 만들 경우에 경험하게 되는 문제점들 및 해결 방법을 다룬다. 봇의 컨텍스트 엔진과 대화 엔진간의 다형성 모델을 구현하고 연결하는 아이디어와 함께 자연어 처리 및 연속 대화 구현, 어법 처리 등을 어떻게 모델링할 수 있는 지에 대한 아이디어 및 구현과 팁을 공유하고자 한다.
This book is about new technology of Openio's ChatGPT.
Published By:
http://braddicksholidaycentre.co.uk/
Braddicks Holiday Centre is a holiday park located in Westward Ho North Devon offering family holidays and pet friendly accommodation next to the seaside.
Interested in building real-time apps like gmail or facebook? In many cases we need to notify clients immediately when something happens: stock prices, online games, chats, betting apps, etc. In this talk we will discuss how to design and build data-streaming API using WebSockets and other related technologies. We plan to cover problems and challenges and overview several libraries and products in this area.
Ant Colony Optimization for Load Balancing in CloudChanda Korat
here the presentation gives the natural behavior of ants and how the that logic is applicable to cloud for load balancing is discussed here with detailed literature survey.
Distributed data-intensive systems are increasingly designed to be only eventually consistent. Persistent data is no longer processed with serialized and transactional access, exposing applications to a range of potential consistency and concurrency anomalies that need to be handled by the application itself. Controlling concurrent data access in monolithic systems is already challenging, but the problem is exacerbated in distributed systems. To make it worse, only little systematic engineering guidance is provided by the software architecture community regarding this issue.
Susanne shares her experiences from different case studies with industry clients, and novel design guidelines developed by using action research. You will learn settled and novel approaches to tackle consistency- and concurrency related design challenges.
Cluster computing is a type of computing where a group of several computers are linked together, allowing the entire group of computers to behave as if it were a single entity. There are a wide variety of different reasons why people might use cluster computing for various computer tasks. It s also used to make sure that a computing system will always be available. It is unknown when this cluster computing concept was first developed, and several different organizations have claimed to have invented it.
Slides from QConSF Nov 19th, 2011 focusing this time on describing the globally distributed and scaled industrial strength Java Platform as a Service that Netflix has built and run on top of AWS and Cassandra. Parts of that platform are being released as open source - Curator, Priam and Astyanax.
Building a cost-effective and high-performing public cloudcloudprovider
Sander Cruiming, founder of Cloud Provider, shows how to build a cost-effective and high-performing public cloud to meet the today's high demands and requirements for cloud infrastructure. Presented at the HPC Advisory Council in Lugano, Switzerland on the 14th of March, 2013.
The corresponding Youtube video with audio: http://www.youtube.com/watch?v=fhRH_yIlM7g
Please check for more information: http://www.cloudprovider.net
An MPI-IO Cloud Cluster Bioinformatics Summer Project (BDT205) | AWS re:Inven...Amazon Web Services
Researchers at Clemson University assigned a student summer intern to explore bioinformatics cloud solutions that leverage MPI, the OrangeFS parallel file system, AWS CloudFormation templates, and a Cluster Scheduler. The result was an AWS cluster that runs bioinformatics code optimized using MPI-IO. We give an overview of the process and show how easy it is to create clusters in AWS.
An insight into how publishers use Amazon Web Services and the benefits that our services bring to their business.
Phil Fitzsimons, Solution Architect, AWS
With the introduction of AWS OpsWorks, you can now build and manage your application stacks with the finesse and control of Chef recipes. OpsWorks compliments the AWS management frameworks and in this session we'll dive deep on how to use OpsWorks and how to get the best from the framework.
Thomas Metschke, Technical Program Manager, AWS
Rik Heywood, Technical Director, Workfu
AWS Summit 2013 | Auckland - Extending your Datacentre with Amazon VPCAmazon Web Services
As more organisations seek to leverage the power and benefits of the cloud, they also need to combine new systems with existing on-premise systems. Services such as Amazon Virtual Private Cloud (VPC) and AWS Direct Connect enable AWS customers to combine on-premise and cloud-based resources easily and effectively. This session will walk customers through the 4 main patterns of connectivity and will include a "real time" demonstration of how easy it is to setup your own VPC and start working in your own private section of the AWS Cloud.
AWS Canberra WWPS Summit 2013 - Extending your Datacentre with Amazon VPCAmazon Web Services
As more organisations seek to leverage the power and benefits of the cloud, they also need to combine new systems with existing on-premise systems. Services such as Amazon Virtual Private Cloud (VPC) and AWS Direct Connect enable AWS customers to combine on-premise and cloud-based resources easily and effectively. This session will walk customers through the 4 main patterns of connectivity and will include a "real time" demonstration of how easy it is to setup your own VPC and start working in your own private section of the AWS Cloud.
AWS provides multiple storage options to meet your varying needs. This presentation provides an overview of how AWS Cloud storage services can be used to support application development and delivery, backup, archive, disaster recovery, and virtualized compute.
Developing applications on Amazon Web Services (AWS) or moving your business into the cloud is more straightforward than you think. Whether you are a developer eager to learn new skills, a solutions architect who wants to solve existing technology problems, the IT professional who wants access to cost-effective, on-demand computing resources, this workshop is for you.
These slides feature some of the most popular Amazon Web Services: Amazon Elastic Compute Service (EC2), Amazon Simple Storage Service (S3), Amazon CloudFront, Amazon Elastic Block Storage (EBS) and Amazon Relational Database Service (RDS).
Amazon EC2 Demo: http://youtu.be/kMExnVKhmYc
AWS Summit 2013 | Singapore - Extending your Datacenter with Amazon VPCAmazon Web Services
As more organizations seek to leverage the power and benefits of the cloud, they also need to combine new systems with exiting on-premises systems. Services such as Virtual Private Cloud, VPN and DirectConnect enable AWS customers to combine on-premises and cloud-based resources easily and effectively. This session will walk customers through the 4 main patterns of connectivity and will include a ""real time"" demonstration of how easy it is to setup your own VPC and start working in your own private section of the AWS Cloud.
Optimizing for Cost in the AWS Cloud - 5 Ways to Further Save - AWS Summit 20...Amazon Web Services
AWS Technology Evangelist Jinesh Varia discusses how you can optimize your costs in the cloud and further reduce you cost and save. He discusses a number of data points of how customers are saving money with AWS
Try Amazon cloud. Avail our 360 degree report on your Benefits / ROI / Migration Timeline exclusive for your business. Amazon Cloud can bring your maintenance/investment reduced, giving worlds efficient IT infrastructure that is required to meet your scaling up/down online software application/data access and processing speed for your business you have/use may get moved to Amazon cloud with all required compliances. We request an hour time slot with your stakeholders, next week, between Monday and Friday , 8am and 5pm EST.
AWS has different pricing models to match your needs. One example is the different instance types available such as On-Demand, Reserved and Spot Instances. Customers can develop cost-saving strategies based upon their usage patterns, models and growth expectations. In some cases, a set of larger instances can be cheaper than multiple small instances. Learn how to size your AWS applications to maximize your use and minimize your spend. Companies such as Pinterest take very active roles to constantly reduce their spend; learn how they do it and develop your own cost-saving approaches.
Intended for customers who have (or will have) thousands of instances on AWS, this session is about reducing the complexity of managing costs for these large fleets so they run efficiently. Attendees will learn about common roadblocks that prevent large customers from cost optimizing, tools they can use to efficiently remove those roadblocks, and techniques to monitor their rate of cost optimization. The session will include a case study that will talk in detail about the millions of dollars saved using these techniques. Customers will learn about a range of templates they can use to quickly implement these techniques, and also partners who can help them implement these templates.
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.
3. Cloud Economics – Agile ROI
Get a faster Return by Speeding up Investment
Observe
Act Orient
Rapid innovation by
speeding up the Decide
OODA loop
Try, fail, try again, succeed
4. « Want to increase innovation?
Lower the cost of failure »
Joi Ito
5. Experiment Often & Adapt Quickly
• Cost of failure falls dramatically
• Return on (small incremental)
Investments is high
• More risk taking, more innovation
• More iteration, faster innovation
8. Netflix Examples
• Brazilian Proxy Experiment
• No employees in Brazil, no “meetings with IT”
• Deployed instances into two zones in AWS Brazil
• Experimented with network proxy optimization
• Decided that gain wasn’t enough, shut everything down
• European Launch using AWS Ireland
• No employees in Ireland, no provisioning delay, everything worked
• No need to do detailed capacity planning
• Over-provisioned on day 1, shrunk to fit after a few days
• Capacity grows as needed for additional country launches
15. www.MyWebSite.com
(dynamic data)
Amazon Route 53
media.MyWebSite.com
(DNS)
(static data)
Elastic Load
Balancer
Amazon
Auto Scaling group : Web Tier CloudFront
Amazon EC2
Auto Scaling group : App Tier
Amazon RDS Amazon Amazon S3
RDS
Availability Zone #1
Availability Zone #2
16. Hourly CPU Load
14
12
10
8
Load
6 25% Savings
4
2
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
24 Hours in a
Day
Optimize by the time of day
17. Web Servers 50% Savings Weekly CPU Load
1 5 9 13 17 21 25 29 33 37 41 45 49
Weeks in a Year
Optimize during a year
18. Architectures that follows the money
How your architecture scales ∝ Customer Traffic
How your architecture scales ∝ How you make money
19. Mastering the Trade-offs
How many $/customer are you willing to spend for
50% better latency to customers that will
increase in conversion to paid customers (or
more signups) by 10%?
How many $/customer are you willing to spend for
100 more renders per minute (10% reduction in
wait time for customers) resulting in 50% more
reach (viral awareness)?
How many $/job are you willing to spend for 30%
more faster results for your analytics job?
20. Netflix’s use of Custom Metrics
Business
SLAs
Requests
Your User Timeout
PUT 2 weeks
App Latency
Resp time
Concurrent Alarm
Amazon CloudWatch
Users
Instance
Custom
Metrics
via Servo
“Increase, Decrease, Shrink, Expand your Instances ”
25. Cost and
wasted wasted
Demand capacity capacity
600k
Maintaining
on-premise
infrastructure wasted
capacity
for peak 300k
demand is wasted
lost
customers, order
ed hardware
expensive capacity
200k
Capacity of resources
Actual demand
Q1 Q2 Q3 Q4 Q1
Time
26. When Comparing TCO…
Place
Make sure that Power
you are including
Pipes
all the cost factors
into consideration People
Patterns
27. Save more when you reserve
On-demand Reserved Spot
Dedicated Instances
Instances Instances Instances
• Pay as you go • One time low • Requested Bid • Standard and
upfront fee + Price and Pay as Reserved
discounted hourly you go • Multi-Tenant
costs • Price change Single Customer
• Zero commitment
• Upto 71% savings every hour based • Ideal for
over On-Demand on unused EC2 compliance and
capacity regulatory
workloads
Billing Options
28. Business-aligned Architectures = Savings
Free Offering Premium Offering
• Optimize for reducing cost Optimized for Faster response times
• Acceptable Delay Limits No Delays
Implementation Implementation
• Use Spot Instances first Paid Subscriptions ∝ RIs
• Use on-demand Instances, if Use on-demand Instances during
Spot is not available in 15 min weekends (high traffic)
Bid higher in spot if On-Demand is
not available
29. Save more when you reserve
On-demand Reserved
Instances Instances Light
Utilization RI
• Pay as you go • One time low
upfront fee + 1-year and Medium
discounted 3-year terms Utilization RI
hourly costs
• Zero Heavy
commitment • Upto 71% Utilization RI
savings over On-
Demand
30. Break-even point
Utilization Ideal For Savings over
(Uptime) On-Demand
d
s 10% - 40% Disaster Recovery
ow
Light
Utilization RI
(>3.5 < 5.5
months/year)
(Lowest Upfront) 56%
+ 40% - 75% Standard Reserved
1-year and 3-
year terms
Medium
Utilization RI
(>5.5 < 7 months/year) Capacity 66%
s >75% Baseline Servers
Heavy
Utilization RI
(>7 months/year) (Lowest Total Cost) 71%
r On-
31. Mix and Match Reserved Types and On-Demand
12
10
On-Demand
8
Instances
6
Light RI Light RI Light RI Light RI
4
2
Heavy Utilization Reserved Instances
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Days of Month
32. Netflix Concept of Reserving Capacity for Maximum Savings
Occasional Spikes On-Demand On-Demand
Heavy RI Heavy RI
Normal Usage Light RI Light RI
Billing Billing
us-west region us-east region
33. Netflix Concept of Reserving Capacity for Maximum Savings
Occasional Spikes On-Demand On-Demand
Heavy RI Light RI
Normal Usage Heavy RI Light RI
Billing Billing
us-west region us-east region
34. Key Takeaways on Cost-Aware Architectures….
#1 Business Agility by Rapid Experimentation = Increased Revenue
#2 Business-driven Auto Scaling Architectures = Savings
#3 Mix and Match Reserved Instances with On-Demand = Savings
35. Usage Patterns: Variety of Applications and Environments
Every Company has…. Every Application has….
LOB and Products Production Fleet
Fleet
Dev Fleet
Marketing Site Test Fleet
Intranet Site Staging/QA
BI and DW Perf Fleet
CRM DR Site
Training Sites
36. Consolidated Billing: Single payer for a group of
accounts
One Bill for multiple accounts
Easy Tracking of account
charges (e.g., download CSV of
cost data)
Volume Discounts can be
reached faster with combined
usage
Reserved Instances are shared
across accounts (including RDS
Reserved DBs)
37. Over-Reserve the Production Environment
Total Capacity
Production Env.
100 Reserved
Account
QA/Staging Env.
0 Reserved
Account
Perf Testing Env.
0 Reserved
Account
Development
Env. 0 Reserved
Account
Storage Account 0 Reserved
38. Consolidated Billing Borrows Unused Reservations
Total Capacity
Production Env.
68 Used
Account
QA/Staging Env.
10 Borrowed
Account
Perf Testing Env.
6 Borrowed
Account
Development
Env. 12 Borrowed
Account
Storage Account 4 Borrowed
39. Consolidated Billing Advantages
• Production account is guaranteed to get burst capacity
• Reservation is higher than normal usage level
• Requests for more capacity always work up to reserved limit
• Higher availability for handling unexpected peak demands
• No additional cost
• Other lower priority accounts soak up unused reservations
• Totals roll up in the monthly billing cycle
40. Key Takeaways on Cost-Aware Architectures….
#1 Business Agility by Rapid Experimentation = Increased Revenue
#2 Business-driven Auto Scaling Architectures = Savings
#3 Mix and Match Reserved Instances with On-Demand = Savings
#4 Consolidated Billing and Shared Reservations = Savings
41. Continuous optimization in your
architecture results in
recurring savings
as early as your next month’s bill
42. Right-size your cloud: Use only what you need
An instance type
for every purpose
Assess your
memory & CPU
requirements
• Fit your
application to
the resource
• Fit the resource
to your
application
Only use a larger
instance when
needed
43. Reserved Instance Marketplace
Buy a smaller term instance Sell your unused Reserved Instance
Buy instance with different OS or type Sell unwanted or over-bought capacity
Buy a Reserved instance in different region Further reduce costs by optimizing
44. Instance Type Optimization
Older m1 and m2 families Latest m3 family
Slower CPUs Faster CPUs (Sandybridge)
Higher response times Lower response times
Smaller caches (6MB) Bigger caches (20MB)
Oldest m1.xl 15GB/8ECU/$0.48 Even faster for Java vs. ECU
Old m2.xl 17GB/6.5ECU/$0.41 New m3.xl 15GB/13 ECU/$0.50
~16 ECU/$/hr 26 ECU/$/hr – 62% better!
Java measured even higher
Deploy fewer instances
45. Key Takeaways on Cost-Aware Architectures….
#1 Business Agility by Rapid Experimentation = Increased Revenue
#2 Business-driven Auto Scaling Architectures = Savings
#3 Mix and Match Reserved Instances with On-Demand = Savings
#4 Consolidated Billing and Shared Reservations = Savings
#5 Always-on Instance Type Optimization = Recurring Savings
46.
47. Follow the Customer (Run web servers) during the day
16
14
No. of Reserved
Instances
No of Instances Running
12
10
8
Auto Scaling Servers
6 Hadoop Servers
4
2
0
Mon Tue Wed Thur Fri Sat Sun
Week
Follow the Money (Run Hadoop clusters) at night
48. Total
Instances
Reserved
Table
14 Types 4 AZ-mappings
Web Launch 40
Unused Hadoop
Application Reservations Fleet
Fleet Calculator
Total Instances
Running now = 100
Total unused Reservations
available = 40 in 2 AZs
(5 min interval)
49. Soaking up unused reservations
Unused reserved instances is published as a metric
Netflix Data Science ETL Workload (Starts after midnight)
• Daily business metrics roll-up
• EMR clusters started using hundreds of instances
Netflix Movie Encoding Workload
• Long queue of high and low priority encoding jobs
• Can soak up 1000’s of additional unused instances
50. Building Cost-Aware Cloud Architectures
#1 Business Agility by Rapid Experimentation = Increased Revenue
#2 Business-driven Auto Scaling Architectures = Savings
#3 Mix and Match Reserved Instances with On-Demand = Savings
#4 Consolidated Billing and Shared Reservations = Savings
#5 Always-on Instance Type Optimization = Recurring Savings
#6 Follow the Customer (Run web servers) during the day
Follow the Money (Run Hadoop clusters) at night
51. Thank you!
Jinesh Varia and Adrian Cockcroft
jvaria@amazon.com @jinman
acockcroft@netflix.com @adrianco
Editor's Notes
Small incremental investments and faster returns gives you an opportunity to innovate quickly
In the old world, Cost of failure is too high,People are afraid to take risks, Innovation suffersTurns out that - we have to be wrong a lot in order to right a lotCloud really helps you to reduce the cost of failure and rapidly iteratingHow many big ticket technology idea can your budget tolerate?
In the cloud world, the cost of failure falls dramatically. One of the greatest value proposition You can launching multiple environments in parallel, Just yesterday, I was extremely curious about Play2 framework and whether it will support by new idea, I spun up 3 parallel environments one with a different database flavor to it, and when I was done and know which one I wanted, I was able to kill the other two and run quickly.
Turn ideas into businesses quickly, gain competitve advantage and releasing your products quickly and not only increasing revenue but also market share.
One of the greatest value proposition is you can start out small risk-free and commitment free with on-demand resources (because you have no clue how your app is going to perform and how much capacity you will need initially) and as you usage grows and you learn about traffic pattern, you reduce your costs by reserving capacity. Dropship your application to new geographies.Since we’ve invested in facilities around the world, we can offer you global reach at a moment’s notice. It’s cost prohibitive to put your own data center where all your customers are, but with AWS, you get the benefit without having to make the huge investment.
Only happens in the cloud
Shrink your server fleet from 6 to 2 at night and bring back
Build websites that sleep at night. Build machines only live when you need it
Our strategy of pricing each service independently gives you tremendous flexibility to choose the services you need for each project and to pay only for what you use
Personal Optimization Assistant
Netflix now serves 2x the customer traffic with the same amount of AWS resources as deployed 10 months ago
Reduced TCO remains one of the core reasons why customers choose the AWS cloud. However, there are a number of other benefits when you choose AWS, such as reduced time to market and increased business agility, which cannot be overlooked.
They have 2 offerings: free and premium. The free case they want to minimize cost. They have the ability to have some delay in the service while they transcode the data. So, they set a maximum of $x on the amount they would pay for an hour, and use Spot for the task. If they haven’t gotten capacity in a long time, they choose to start in On-Demand. The premium case they want the media encoding to happen immediately. So, they purchase Reserved Instances to optimize their expected level of demand (note breakeven is around 30% utilization, so buying more RIs may make sense). Then, they use On-Demand for elasticity. If they can’t get the On-Demand when they need it, they try in Spot (e.g. you can get capacity not available anywhere else). In all, they have optimized for their SLA for the premium offering, and minimized cost in their free offering. Both are legitimate scenarios, and AWS is the only provider to support the pricing models to allow them to do it.
No Enterprise has only Steady State Workloads.In fact, no system is entirely steady state.
You should use Consolidated Billing for any of the following scenarios:You have multiple accounts today and want to get a single bill and track each account's charges (e.g., you might have multiple projects, each with its own AWS account).You have multiple cost centers to track.You've acquired a project or company that has its own existing AWS account and you want to consolidate it on the same bill with your other AWS accounts.
You should use Consolidated Billing for any of the following scenarios:You have multiple accounts today and want to get a single bill and track each account's charges (e.g., you might have multiple projects, each with its own AWS account).You have multiple cost centers to track.You've acquired a project or company that has its own existing AWS account and you want to consolidate it on the same bill with your other AWS accounts.
You should use Consolidated Billing for any of the following scenarios:You have multiple accounts today and want to get a single bill and track each account's charges (e.g., you might have multiple projects, each with its own AWS account).You have multiple cost centers to track.You've acquired a project or company that has its own existing AWS account and you want to consolidate it on the same bill with your other AWS accounts.
Cloud is highly cost-effective because you can turn off and stop paying for it when you don’t need it or your users are not accessing. Build websites that sleep at night