Artificial Intelligence and the Data Center
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Artificial Intelligence applications are proliferating within all areas of society. This presentation explores the potential AI applications within the data center and how they will impact applications and operations in the future.
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Presentation for the 45 min. + QA talk I gave at HIIT seminar on 13 May 2013 for local data science researchers.
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As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Enterprise focusing on the modernization of data analytics, the AI ladder and AI life cycle and infrastructure architecture considerations. We will conclude by viewing the benefits and innovation of running your modern AI and Data Analytics applications such as SAS Viya and SAP HANA on IBM Power Systems and IBM Storage in hybrid cloud environments.
"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Pr...
"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Pr...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-baidu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Ren Wu, former distinguished scientist at Baidu's Institute of Deep Learning (IDL), presents the keynote talk, "Enabling Ubiquitous Visual Intelligence Through Deep Learning," at the May 2015 Embedded Vision Summit.
Deep learning techniques have been making headlines lately in computer vision research. Using techniques inspired by the human brain, deep learning employs massive replication of simple algorithms which learn to distinguish objects through training on vast numbers of examples. Neural networks trained in this way are gaining the ability to recognize objects as accurately as humans.
Some experts believe that deep learning will transform the field of vision, enabling the widespread deployment of visual intelligence in many types of systems and applications. But there are many practical problems to be solved before this goal can be reached. For example, how can we create the massive sets of real-world images required to train neural networks? And given their massive computational requirements, how can we deploy neural networks into applications like mobile and wearable devices with tight cost and power consumption constraints?
In this talk, Ren shares an insider’s perspective on these and other critical questions related to the practical use of neural networks for vision, based on the pioneering work being conducted by his former team at Baidu.
Note 1: Regarding the ImageNet results included in this presentation, the organizers of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have said: “Because of the violation of the regulations of the test server, these results may not be directly comparable to results obtained and reported by other teams.” (http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015)
Note 2: The presenter, Ren Wu, has told the Embedded Vision Alliance that “There was some ambiguity with the rules. According to the ‘official’ interpretation of the rules, there should be no more than 52 submissions within a half year. For us, we achieved the reported results after 200 tests total within a half year. We believe there is no way to obtain any measurable gains, nor did we try to obtain any gains, from an 'extra' hundred tests as our networks have billions of parameters and are trained by tens of billions of training samples.”Democratizing Data Science in the Enterprise
This session presents a series of best practices for delivering enterprise data science solutions in the real world.
Presentation v3
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
Large scale computing
Old talk I gave as guest lecture at USC, wanted to share with folks if they find it useful.
IoT and Big Data
The impact of emerging IoT Technology and BigData. This is the slide presentation I did at the http://globalbigdatabootcamp.com/speakers/sanjay-sabnis/
Big Data on OpenStack
The massive computing and storage resources that are needed to support big data applications make cloud environments an ideal fit. In this session, you'll learn how to build your big data "database on-demand" using MongoDB, Cassandra, Solr, MySQL, or any other big data solution, as well as manage your big data application using a new open source framework called “Cloudify.” All this, on top of the OpenStack cloud.
Machine Learning Infrastructure
As data science workloads grow, so does their need for infrastructure. But, is it fair to ask data scientists to also become infrastructure experts? If not the data scientists, then, who is responsible for spinning up and managing data science infrastructure? This talk will address the context in which ML infrastructure is emerging, walk through two examples of ML infrastructure tools for launching hyperparameter optimization jobs, and end with some thoughts for building better tools in the future.
Originally given as a talk at the PyData Ann Arbor meetup (https://www.meetup.com/PyData-Ann-Arbor/events/260380989/)
Demystifying Machine Learning and Artificial Intelligence
By Rob Baxter, EPCC.
This talk was presented at the EPCC-CeeD event 'Advanced technologies for Industry 4.0' in September 2018.
5 Things that Make Hadoop a Game Changer
5 Things that Make Hadoop a Game Changer
Webinar by Elliott Cordo, Caserta Concepts
There is much hype and mystery surrounding Hadoop's role in analytic architecture. In this webinar, Elliott presented, in detail, the services and concepts that makes Hadoop a truly unique solution - a game changer for the enterprise. He talked about the real benefits of a distributed file system, the multi workload processing capabilities enabled by YARN, and the 3 other important things you need to know about Hadoop.
To access the recorded webinar, visit the event site: https://www.brighttalk.com/webcast/9061/131029
For more information the services and solutions that Caserta Concepts offers, please visit http://casertaconcepts.com/
Entity embeddings for categorical data
Word embeddings are common for NLP tasks, but embeddings can also be used to learn relations among categorical data. Deep learning can be useful also for structured data, and entity embeddings is one reason why it makes sense. These are slides from a seminar held in Sbanken.
Challenges of Operationalising Data Science in Production
The presentation topic for this meet-up was covered in two sections without any breaks in-between
Section 1: Business Aspects (20 mins)
Speaker: Rasmi Mohapatra, Product Owner, Experian
https://www.linkedin.com/in/rasmi-m-428b3a46/
Once your data science application is in the production, there are many typical data science operational challenges experienced today - across business domains - we will cover a few challenges with example scenarios
Section 2: Tech Aspects (40 mins, slides & demo, Q&A )
Speaker: Santanu Dey, Solution Architect, Iguazio
https://www.linkedin.com/in/santanu/
In this part of the talk, we will cover how these operational challenges can be overcome e.g. automating data collection & preparation, making ML models portable & deploying in production, monitoring and scaling, etc.
with relevant demos.
What Managers Need to Know about Data Science
What technical, statistical and communication skills a manager should know to manage a data science project
How and why you need to build a big data lab
Presentation to Usergroup SIG about building and deploying a lab environment on Cloud platforms to provide best value
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NYC Open Data Meetup-- Thoughtworks chief data scientist talk
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"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Pr...
"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Pr...
Demystifying Machine Learning and Artificial Intelligence
Demystifying Machine Learning and Artificial Intelligence
Challenges of Operationalising Data Science in Production
Challenges of Operationalising Data Science in Production
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Just add water: The Resource Issues of Water Based Cooling
The use of water-based cooling methods is becoming an increasingly important decision for new data centers and their operators. This presentation discusses the issues associated with water based cooling and other cooling alternatives
The Stratification of Data Center Responsibilities
New end user standards of satisfaction are forcing the traditional data center network structure to change. This presentation discusses the new multi-tiered structure that will define data center networks in the coming years.
Wearables in data centers
Wearable devices are revolutionizing data center operations. Information that traditionally was included in multiple volumes can now be made available at a technician's fingertips. This presentation provides a case study as to how one company is improving its operational performance thru wearable technology
Data Centers and IoT
The high volume data processing demands of IoT exceed the capabilities of the majority of today's data centers. This presentation examines the issues that must be addressed to ensure a successful IoT implementation.
Twin sons of different mothers latency and bandwidth 2
High volume (ex: Iot) and large rich packet applications are driving requirements for ever lower latency and increased bandwidth. This presentation discusses the issues and remedies to address these two important data center considerations.
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In this presentation Chris Crosby discusses how the industry is evolving based on new technological innovations
Data center commissioning 101
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The Six Month Data Center
In this presentation Chris Crosby discusses the elements necessary to deliver a data center in six months
Unintended consequences
In this presentation Chris Crosby explains why some data center "enhancements" have some unpleasant side effects
Buyer beware
In this presentation Chris Crosby discusses the the issues that end users need to be aware of before they choose their next data center provider
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重新申请毕业证书(RMIT毕业证)皇家墨尔本理工大学毕业证成绩单精仿办理
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国外毕业证学位证成绩单如何办理：
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国外毕业证学位证成绩单办理方法：
1客户提供办理莫纳什大学莫纳什大学毕业证文凭证书信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信176555708
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
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◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
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本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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degree，Transcripts（一对一服务包括毕业院长签字,专业课程,学位类型,专业或教育领域,以及毕业日期.不要忽视这些细节.这两份文件同样重要!毕业证成绩单文凭留信网学历认证！）如果您是以下情况，我们都能竭诚为您解决实际问题：【公司采用定金+余款的付款流程，以最大化保障您的利益，让您放心无忧】
1、在校期间，因各种原因未能顺利毕业，拿不到官方迪肯大学毕业证+微信176555708；
2、面对父母的压力，希望尽快拿到；
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1.如果您只是为了的应付父母亲戚朋友看下迪肯大学毕业证，那么办理一份学位即可
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诚招代理：本公司诚聘当地合作代理人员，如果你有业余时间，有兴趣就请联系我们。
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2.Cellular Networks_The final stage of connectivity is achieved by segmenting...
A cellular network, frequently referred to as a mobile network, is a type of communication system that enables wireless communication between mobile devices. The final stage of connectivity is achieved by segmenting the comprehensive service area into several compact zones, each called a cell.
Understanding User Behavior with Google Analytics.pdf
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
Tracking User Engagement:
Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
Conversion Rate Optimization:
Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
国外证书(Lincoln毕业证)新西兰林肯大学毕业证成绩单不能毕业办理
退学办理新西兰林肯大学毕业证【微信176555708】学历认证怎么做：原版仿制新西兰林肯大学电子版成绩单毕业证认证【新西兰林肯大学毕业证成绩单】、新西兰林肯大学文凭证书成绩单复刻offer录取通知书、购买Lincoln圣力嘉学院本科毕业证、【新西兰林肯大学毕业证办理Lincoln毕业证书哪里买】、新西兰林肯大学 Offer在线办理Lincoln Offer新西兰林肯大学Bachloer Degree。1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信176555708我们有专业老师帮你查询）；
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6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
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高精端提供以下服务：
一：新西兰林肯大学新西兰林肯大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信176555708）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。
Internet of Things in Manufacturing: Revolutionizing Efficiency & Quality | C...
This article delves into how the Internet of Things in manufacturing is reshaping the industry, its benefits, challenges, and future prospects.
原版仿制(uob毕业证书)英国伯明翰大学毕业证本科学历证书原版一模一样
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本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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可查真实(Monash毕业证)西澳大学毕业证成绩单退学买
办理假西澳大学毕业证【微信176555708】购买,办理Monash成绩单,Monash毕业证制作【微信176555708】【西澳大学毕业证】，Monash毕业证购买，Monash学位证，西澳大学学位证【Monash成绩单制作】【Monash毕业证文凭 Monash本科 澳洲学历认证原版制作【diploma certificate degree transcript 】【留信网认证，本科，硕士，海归，博士，排名，成绩单】代办国外（海外）澳洲、韩国、加拿大、新西兰等各大学毕业证。 ?我们对海外大学及学院的毕业证成绩单所使用的材料，尺寸大小，防伪结构（包括：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。 文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）都有原版本文凭对照。#一整套西澳大学文凭证件办理#—包含西澳大学西澳大学毕业证成绩单学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。
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二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
国外毕业证学位证成绩单办理方法：
1客户提供办理西澳大学西澳大学毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
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Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdf
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
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Artificial Intelligence and the Data Center
- 1. AI and the Data Center Jose Ruiz, VP, Engineering
- 2. Hype, Fear and Where We Are • We are a long, long, long, way from SkyNet • The possibilities are legion • But don’t believe everything that you hear • Ex: Your job isn’t going away tomorrow • AI is still in its infancy • Including within the data center • Current focus is on efficiencies 2
- 3. What is AI? • Artificial Intelligence is actually an “umbrella” term • Three components: • Neural networks • Machine learning • Deep learning • The AI “stack” • Higher level functional capability as you move up the stack 3
- 4. The Stack Deep learning Machine learning Neural networks • Deep learning • Analyzes data at different abstractions • Uses multiple neural network layers • Machine learning • Learn through absorption of information • Refine through algorithms to determine “optimal” solution • Neural networks • Computer to look like a brain • Multiple nodes • Collectively can be “taught” to solve higher level problems 4
- 5. The Stack in Action • “Intelligence” builds as you move up the stack • Learnings become more global • Information gathered in neural network nodes • Higher order patterns learned at machine learning level • Complicated data is analyzed by breaking it into component parts • Long iterative process 5 Deep learning Machine learning Neural networks
- 6. Limitations • Nuances • Difficulty with inferences • Don’t recognize causal relationships • Ex: Symptoms and disease • Doesn’t have “common sense” • Couldn’t predict and solve a problem based on activities with out detailed algorithms • Would limit the availability of “off the shelf” solutions • Now, not necessarily forever 6 (Someday)
- 7. AI in the Data Center • Currently company specific • Province of the big boys: • Google, etc. • Due to long ”learning cycle” • Example: • Google has used AI to reduce energy use by 40% • Analysis of large volumes of data • Energy used per component, outside air temp, etc. • They were capturing all along • “Taught” by algorithm to analyze interplay between variables • Determined optimal relationships 7
- 8. The Future of AI in the Data Center • Expect ”productized” applications within 5 years • Will be subtle • Incorporated into existing offerings • Ex: DCIM • 80-90% intelligence “built-in” • Customer will be responsible for fine tuning • Early focus • Energy usage and efficiency • Cooling • Server optimization 8
- 9. Summary • Artificial intelligence is an umbrella term • Progressive functionality via stack • Neural networks • Machine learning • Deep learning • In its infancy for data centers • End user proprietary • “Productization” is coming • But not for awhile • Will be subtle • Aid in increasing automation 9