Infrastructure Solutions for Deploying AI/ML/DL Workloads at Scale
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Learn how the new Cisco UCS C480 ML Server delivers the high-performance compute you need without introducing technology silos.
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Infrastructure Solutions for Deploying AI/ML/DL Workloads at Scale
- 1. Vikas Ratna, Product Manager, Cisco – vratna@cisco.com Han Yang, Product Manager, Cisco – hanyang@cisco.com November 7, 2018 Infrastructure Solutions for Deploying AI/ML/DL Workloads at Scale
- 2. AI/ML/DLWorkload In Enterprises • Why Now? Why Beyond Hype? • Customer Challenges & Cisco UCS Strategy • Solutions and Partnerships • Demo – Deploying at Scale • Wrap up Agenda
- 3. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Partner Confidential AI/ML/DL Act of Artificial Intelligence/Inferencing Machine Learning Deep Learning - Machines learn from data Execute Defined Action Produce Model - Then based on learning, Machines make decisions OR predict/infer things when new data comes in …mostly in real time a.k.a Inferencing a.k.a Training (Simple or Deep)
- 4. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Partner Confidential• 277X data created by IoE devices vs. end users – source: 2014 Cisco® Global Cloud Index • By 2020, there will be 5200 GB of data for every person on earth – source: 2012 Digital Universe Study conducted by IDC and sponsored by EMC (see: http://www.computerworld.com/article/2493701/data-center/by-2020--there-will-be-5-200-gb-of-data-for-every-person-on-earth.html) • 180 billion mobile app downloads by 2015 – source: 2011 IDC Study: https://www.smaato.com/blog-180billiondownloads/ 277X Data created by IoT devices vs. end users 30M New devices connected every week 180B Mobile apps downloaded in 2015 40% Of all data will come from sensor data by 2020 5TB+ Of data per person by 2020 4.2B Web filtering blocks per day Why Now? First…The Data Explosion - Volume, Velocity, Variety Unrealistic to use traditional analytics to gain meaningful insight at speed. A New Approach is Needed!!!
- 5. Why Now? Second –Perfectly timed arrival of Data Infra, Compute, Network and ML Innovation Improved Data Collection Infrastructure Improved Computing Power & Network Advancement in ML Frameworks Hadoop w/Apache Spark Faster GPUs, CPUs & Network TensorFlow, PyTorch, Caffe, Theano
- 6. Beyond Hype…Solving Biz Needs AcrossAll IndustryVerticals.. Retail Security and Defense Media and Entertainment HealthcareFinanceAI Task Business Question Is "it" present or not? What type of thing is "it"? To what extent is "it" present? What is the interpretation? What is the likely outcome? What will satisfy the objective? Detection Classification Segmentation Natural Language Processing Prediction Recommenda tions Identify Access Anomalies Fraud detection Sentiment Analysis Chatbot Advisors Credit Profiling Algorithmic Trading Indication of Anomaly in Scan Diagnostics – Tumor? Condition Analysis Expert Diagnosis from Notes Length of Stay Forecasting Treatment Recommenda tions Content Based Search Content Labeling Improved Product Placement Video Captioning Targeted Content Generation Content Recommenda tions Identify Security Breaches Facial Recognition Crowd Analytics Real Time Language Translation Equipment Health Assessment Risk Management Events in Store Surveillance Returning vs New Shoppers Segment by Customers Actions In Store Personal Assistants Customer Churn and Retention "Magic Mirror" Manufacturing Detect Manufacturing Flaws Robots to Track Objects Sort Components by Quality Assembly Build Instruction Translation Proactive Machine Maintenance Assembly Process Improvements
- 7. High Level View of Workflow When Using AI/ML In Production Data Infrastructure Prepare Data Train a Model Evaluate the Model Deploy, Inference & Improve Data is at the heart. More data trumps better algorithms Data and business questions determine ML algorithm(s) Data can come from anywhere and is not usually in a state where it is ready to use for training ML models Build a model and feed the model with prepared training data so that it can learn to make inferences Test the trained model performance and accuracy by analyzing inference feedback Deploy Model for inferencing. Evaluate and Improve accuracy by either selecting different algorithms and/or retraining model with more data Big Data ML/DL Framework Training Inference
- 8. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Customer AI Challenges
- 9. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Customer AI Challenges • C-Suite wants an AI strategy • Marketing wants to include AI in everything • Worried about falling behind and competition passing • Not sure what problems to solve; not enough expertise • Everyone is pitching to them and they are overwhelmed ML Value StackProfessional services Consulting Practices Implementation Practices Learning/training Practices Servers, Appliance, storage ML Framework/Library Silicon Drivers OS, run time environ Intel® Math Kernel Library (MKL, MKL- DNN) Intel® NervanaTM Graph Data Science / ML Platform Data Prep >> Modelling support >> Deployment Roots from open source Proprietary Infra management Data mgmt Infra mgmt ML application and API Looking For Guidance, Simplification And this is only one part of infrastructure. This needs to connect and harmoniously co-exist with infrastructure that generates data!
- 10. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Key Challenges for IT Massive & Active Data Sets Volume, Velocity, and Variability of AI Workloads at Scale Demand New Data Center Architectures Uncharted Territory Rapidly Evolving AI/ML Ecosystem and Requirements; Skill Shortages in Data Science and IT Seeking AI/ML Infra which is: Silo-less, Simple to manage at scale, Validated for new workload Operational Efficiency Distributed Data Sources, New Workloads Risk Operational Silos and Complexity
- 11. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Cisco Compute Strategy & Product Portfolio
- 12. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Recap: Cisco Unified Computing System (UCS) Momentum Source: 1 IDC, 2017 Q4, Sep 2017, Vendor Revenue Share Source: As of Cisco Q4FY17 earnings results. Data Center Revenue is defined as Cisco UCS and Nexus 1000V Integrated Infrastructure (Cisco UCS, Nexus, FlexPod) #1 Hyperconverged vendor #3 Global Revenue Market Share in x86 Blades #2 World Record Performance Benchmark 150+ Exabyte Total Storage Deployed 1.4 Big Data Revenue Growth in 4 Years 18x Enterprise customers 64,000+ of Fortune 500 Have Invested in UCS >85% HyperFlex Customers 3000+
- 13. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Component Cisco UCS Portfolio: Any Workload. Any Scale. One System. Hyperconverged Infrastructure Converged InfrastructureROBO Fifth Generation UCS HyperFlex Systems UCS Mini E-Series Data Intensive S-Series Storage Optimized C-Series Rack Servers UCS Integrated Infrastructure Solutions CoreEdge Cloud
- 14. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public AI/ML Strategy: Focus on Full Data Life Cycle, Simplicity, and Manageability Model Deployment/ Inference Data Source & Aggregate Cleanup Transformation Training Model Dev Model Validation Model Execution Full portfolio for all AI/ML computing needs Validated solutions with technology partners No Silos: Natural extension of existing computing environment Application runtime at source of demand
- 15. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Inference in Regional and Micro Data Centers Cisco AI/ML - Computing Platforms, Partners, and Solutions UCS C220 Servers HyperFlex C220 Servers UCS C240 Servers HyperFlex GPU Nodes Test/Dev and ML in Private Cloud Deep Learning in DC Core Accelerated Compute Portfolio Software Partners Solution Partners NEW! UCS C480 ML UCS C480 ML
- 16. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Cisco AI/ML Compute Portfolio – Addressing All Aspect Test & Dev and Model Training C240 2 x P4 6 x P4 HyperFlex 240 Deep Learning/ Training C480 Inferencing C/HX 220 C/HX 240 Unified Management Option of GPU Only Nodes 2 x P100/ V100 2 x P100/ V100 Per Node 6 x PCIe P100/ V100 8x V100 with NVLink C480 ML Cisco IMC XML API New
- 17. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Cisco UCS C480 ML Rack Server No-compromise balance of performance and capacity to power AI workloads at scale NEW Prevents Operation Silos: Extends Existing UCS Environments with Consistent, Cloud-Based Management Validated with Popular Machine Learning Software to Accelerate and Simplify AI/ML Projects on Premise Fully Integrated Platform Designed to Accelerate Deep Learning • Eight NVIDIA Tesla SXM2 V100s with NVIDIA NVLink Interconnect • Up to 24 Drives; 182TB • Up to 6 NVMe Drives • Network: Up to 4x100GB • High Availability Design 40,000+ CUDA Cores
- 18. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public 6542 3778 3208 2830 22035 12161 6423 3735 3218 2793 21078 11438 Tensorflow Training: 8GPU (V100) Synthetic Data Real Data UCS C480 ML (8x NVIDIA V100 GPU - Tensorflow training results) Images/sec
- 19. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Cisco Portfolio Alignment With Deployment Lifecycle Use Case Conception Feasibility Study Model Design/Train Model Deployment Model Maintenance Optimized big data solutions for data collection and preparation Distribute and scale AI inference from the data center to the edge Offload and accelerate AI training at scale with performance optimized systems V100 P4 C240 M5 C480 ML M5 C220 / HX220c M5 Quick installation of hardware and software for AI exploration and experimentation V100 C240/ HX240c M5
- 20. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public DC Infrastructure AI/ML/DL Platforms Data / BigData Libraries Framework AI-ML Server / Appliances (CPU or GPU) Simplified DC Infra To Support AI/ML Workload & Cisco AI/ML Apps/ APIs Services Manageability Network DC Servers and Storage Model Deploy/Mgmt Existing UCS Business Several CVDs exist Strategic Partnerships / Cisco AS UCSM, Intersight Exists, Build New Partner Build Validated Designs
- 21. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Solutions
- 22. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public How Important is AI & ML? By 2035, AI technologies are projected to increase business productivity by up to 40% By 2020, insights-driven businesses will steal $1.2T per annum from their less-informed peers 8 out of 10 businesses have already implemented or are planning to adopt AI as a customer service solution by 2020 • $1.2T https://go.forrester.com/wp-content/uploads/Forrester_Predictions_2017_-Artificial_Intelligence_Will_Drive_The_Insights_Revolution.pdf • 8 out of 10 - Oracle - https://www.oracle.com/webfolder/s/delivery_production/docs/FY16h1/doc35/CXResearchVirtualExperiences.pdf • Accenture https://www.accenture.com/us-en/insight-artificial-intelligence-future-growth
- 23. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Relative Change in Cash Flow by AI Adoption Cohort https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-frontier-modeling-the-impact-of-ai-on-the-world- economy?cid=other-eml-alt-mgi-mck-oth-1809&hlkid=5ebe957bb3594f96bedda5695e4664fd&hctky=10366723&hdpid=677435cb-04b0- 445e-afba-4588aa47d2fe
- 24. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Key Challenges Massive & Active Data Sets Volume, velocity, and variability of AI workloads at scale demand new data center architectures Uncharted Territory Rapidly evolving AI/ML ecosystem and requirements; skill shortages in data science and IT The Data Center Follows the Data Distributed data sources and technologies risk operational silos and complexity
- 25. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Eliminating Operational Silos Demystifying AI/ML Stacks Curating top-to-bottom SW and HW stacks with leading ecosystem partners to ensure a faster and more predictable deployment Full array of accelerated computing options for test/dev, training and inference, all unified by cloud-based management Integrating changing data sources as part of a dynamic data pipeline Powering the Full AI Data Lifecycle Cisco Computing Solutions for Machine Learning
- 26. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Data Pipeline for Single Data Source Collect Clean Correlate Train Data Model Result
- 27. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Collect Clean Correlate Train Data Pipeline for Multiple Data Sources Collect Clean Correlate Train Data Model Result Collect Clean Correlate Train Model Collect Clean Correlate Train Model Social Video Model More Data You Are Here Many Verticals
- 28. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Data Pipeline Software Tools Collect Clean Correlate TrainData Model
- 29. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Infrastructure Solutions for the Data Pipeline Ingestion Compute Intensive Storage
- 30. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Data Centric Approach: Expanding Big Data to AI/ML Solutions Cisco Validated Designs Cloudera Data Science Work Bench Kubeflow Hortonworks Hadoop 3.1 Data Lake Hadoop coupled with GPU nodes for deep learning with Jupyter notebook Portable, scalable ML stack enabling rapid development and deployment Integrate Hadoop and AI/ML: YARN Scheduling CPU and GPU with Docker Application Support YARN Scheduler HDFS Hot Tier HDFS Cold TierHDFS
- 31. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Solution Architecture Ecosystem of Partners C480ML Big Data Cluster UCS Hyper Flex Delivered through partners Google AI/ML stack 3rd party AI/ML platforms … UCS platforms… Object storage AI/ML SW platforms Storage and Converged Infra SDS for AI/MLTraditional analytics *Not All Available, In works
- 32. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Cloudera Data Science Workbench Demo
- 33. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Activating Data with the Power of UCS New Cisco Computing Solutions for AI/ML Powering the Full AI Data Lifecycle Accelerating insight and action Unified Architecture Adaptable cloud-managed systems for distributed IT Demystifying AI/ML Stacks Validated solutions with industry leaders
- 34. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public • Cisco UCS Infrastructure for Red Hat OpenShift Container Platform Design Guide • Cisco UCS Integrated Infrastructure for Big Data and Analytics with Cloudera Data Science at Scale • Cisco UCS Integrated Infrastructure for Big Data and Analytics with Hortonworks Data Platform and Hortonworks DataFlow • FlexPod Design Guides • FlashStack Design Guides • VersaStack Design Guides • Cisco UCS Storage Server with Scality Ring • Cisco UCS 3260 Storage Server with SwiftStack Software Defined Object Storage Resources
- 36. Thank you for watching.
Hinweis der Redaktion
- Digital disruption and the resulting explosion in data, applications, and devices are disrupting traditional business models and making it harder to compete successfully than ever. 30M new devices connected every week As just one security indication of the security impact of all of those new devices, 4.2Billion web filtering blocks are created per day Each of those devices will be creating large quantities of data. As a matter of fact, 5TB+ of data will be created per person by 2020 That’s clearly just the tip of the iceberg, 277X data will be created by IoE devices vs end-user And 40% of all data will be created from sensors in IoT devices by 2020 Insights held within these massive volumes of data have the potential to deliver a powerful competitive advantage. The disruption caused by unprecedented growth in devices, mobile apps, data, use of cloud, and security risks are forcing organizations to redefine business processes to compete more effectively, which, in turn, is driving the need for unprecedented agility in their IT architectures and governance. To enable this digital transformation, IT must create/manage an infrastructure that extends from the enterprise data center − to the cloud− to the edge to deliver new levels of speed and efficiency and lay the foundation for ongoing innovation.
- The business value of AI/ML isn’t a debate Why is ML emerging now? There are a couple key reasons. One, it’s getting really cheap to store data. From cheaper hardware to the spread of open source technology like Hadoop to the growth of the cloud, it is now very cheap to store data, resulting in the rise of Big Data. Much of this data is excellent training material for learners and is being packaged for easy use, such at ImageNet and data.gov. But, training a ML is computationally expensive. Fortunately, as seen in our second chart, better CPUs, GPUs, and cloud computing have massively reduced the price of compute. Additionally, during this time, there have been mathematical advances that have made training NNs easier and more effective and a public-private ecosystem – among Universities, companies, and government – that has encouraged rapid progress in the field. The combination of these trends creates an environment that is prime for the growth of machine learning.
- As with many cloud and digital transformation initiatives, the line of business is driving modern AI. A successful AI application starts with a compelling business question. In order to be successful selling AI, you need to start out side of IT and focus on data and business professionals. Industries are creating specific roles that are data driven, a good example is the Chief Medical Information Officer in healthcare. Moving down and across this table: “Is “it” present or not”, is asked in Finance to identify abnormal access to financial data “What type of thing is “it”?”, is asked in healthcare to identify if a tumor is cancerous or benign “To what extent is “it” present?”, is asked in Media and Entertainment to identify if a placed product is impactful “What is the interpretation?”, is asked in Defense to translate a local dialect “What is the likely outcome?” is asked in Retail to identify what customers will buy or not “What will satisfy the objective?” is asked in Manufacturing to identify how to make robots work quicker and build higher quality products
- Simple Learning: K-Nearest Neighbor, Ensemble Techniques, Support Vector Machines, Random Forest, Decision Trees, Linear Regression, Bayesian Techniques, SVMs Deep Learning: Reinforcement Learning, Deep Neural Networks, Recurrent Neural Nets Driving factor for Deep Learning: Big Data (large-scale training data, Algorithmic innovation, Compute infra)
- We believe there are a few key challenges when it comes to AI/ML. The first challenge is traditional data center technology is not designed to handle the data volume, velocity, and variability of AI at production scale. IT teams are struggling to keep up with data scientists needs while data scientists are struggling to operationalize machine learning, new data sources and rapidly shifting software stacks requires. Infrastructure to constantly adapt to these new data-shaped workloads. The massive amount of data required and the ingestion speed also require bandwidth to move the amount of data consumed by AI through all stages of the data lifecycle. “Data is the new Application and IT needs new tools to deliver AI at scale.” The second challenge is many IT departments regard data science software as unstable, unsupported and undebuggable. They are not wrong. The AI ecosystem is still in the early stages resulting in fragmented, unfamiliar, and rapidly evolving machine learning software stacks that increase complexity and risk. There is also a skill shortage. There are not enough data scientists and data engineers. “We’ve taken on the challenge to demystify AI/ML with proven, full-stack solutions developed with industry leaders.” The third challenge is the data center follows the data, IT must integrate new accelerated computing technology across an increasingly distributed landscape. This increasing need for new platforms and data to be analyzed at the source, in the data center and at the edge, can spawn islands of standalone AI/ML servers. IT leaders are not being permitted to add administrative resources as these projects scale to thousands of nodes. Roughly 50% of the DC IT budget goes toward people and software to manage existing infrastructure. A wide variety of traditional servers, software systems, storage have been utilized, and implemented in silos—most system management functions are complicated—designed to try to integrate solutions that were never designed to work together. Hence, they need management solutions that use a common operating model and enable the same resources to be able to manage a distributed, scale-out infrastructure. “The data center is where the data is. As we take you through this briefing you will see how Cisco pairs a full portfolio of AI/ML computing solutions with the simplicity and reach of cloud-managed infrastructure.”
- 思科的融合架构，特别是和NetApp合作的FlexPod，市场占有率是排名世界第一的; 六万两千多的大中小企业在用思科的服务器。 超过85% 的福布斯500强企业在使用思科的UCS系统 而我们的超融合技术HyperFlex在推出两年的时间里，也迅速有了超过2500多家的客户。
- Let’s provide some additional detail on what we are announcing. As I mentioned, in addition to the current portfolio of UCS and Hyperflex systems with GPU support, we are introducing the new UCS C480 ML for model training. Again, I have a couple of detailed slides on this system. We are working with both software partners and channel partners to help IT simplify the deployment of. ML infrastructure. We are collaborating with software vendors to provide proven solutions for AI/ML projects while collaborating with channel partners – Presido, WWT, E+ and others, to build expertise so they can help customer close the skills gap by delivering services to accelerate ML projects.
- Meet the new UCS C480 ML AI server. Intuitive to IT but packs the punch of a supercomputer. Highly integrated but flexible so that it provides Enterprise features like choice of OS and different storage configurations to provide data performance and protection.
- If we want to map UCS to a Deep Learning project we could start with hadoop solutions built on a C240 cluster to collect and look at data to see if we have the data to support answering the compelling business question. Once we know what we want to do, instead on making a huge investment in time and money, we could deploy a small HX cluster with all of the GPUs and tools required to start development and testing of AI applications. Once we start build more complex AI applications in production we can offload training to an AI supercomputer like the C480 ML so that we can keep up with business demands. If we want to deploy trained AI applications for inference of consumption outside of the datacenter and at a remote location like a retail store of manufacturing floor, we can use a small but efficient all in one hyperconverged solution equipped with inference optimized GPUs like the HX220c M5.
- Now, let’s take a look at the products that we are already shipping and some that are coming..
- Stat: $1.2 trillion - Forrester - https://www.forrester.com/report/Predictions+2017+Artificial+Intelligence+Will+Drive+The+Insights+Revolution/-/E-RES133325 Stat: 8 out of 10 - Oracle - https://www.oracle.com/webfolder/s/delivery_production/docs/FY16h1/doc35/CXResearchVirtualExperiences.pdf Stat – 40% - Accenture https://www.accenture.com/us-en/insight-artificial-intelligence-future-growth
- We believe there are a few key challenges when it comes to AI/ML. The first challenge is traditional data center technology is not designed to handle the data volume, velocity, and variability of AI at production scale. IT teams are struggling to keep up with data scientists needs while data scientists are struggling to operationalize machine learning, new data sources and rapidly shifting software stacks requires. Infrastructure to constantly adapt to these new data-shaped workloads. The massive amount of data required and the ingestion speed also require bandwidth to move the amount of data consumed by AI through all stages of the data lifecycle. “Data is the new Application and IT needs new tools to deliver AI at scale.” The second challenge is many IT departments regard data science software as unstable, unsupported and undebuggable. They are not wrong. The AI ecosystem is still in the early stages resulting in fragmented, unfamiliar, and rapidly evolving machine learning software stacks that increase complexity and risk. There is also a skill shortage. There are not enough data scientists and data engineers. “We’ve taken on the challenge to demystify AI/ML with proven, full-stack solutions developed with industry leaders.” The third challenge is the data center follows the data, IT must integrate new accelerated computing technology across an increasingly distributed landscape. This increasing need for new platforms and data to be analyzed at the source, in the data center and at the edge, can spawn islands of standalone AI/ML servers. IT leaders are not being permitted to add administrative resources as these projects scale to thousands of nodes. Roughly 50% of the DC IT budget goes toward people and software to manage existing infrastructure. A wide variety of traditional servers, software systems, storage have been utilized, and implemented in silos—most system management functions are complicated—designed to try to integrate solutions that were never designed to work together. Hence, they need management solutions that use a common operating model and enable the same resources to be able to manage a distributed, scale-out infrastructure. “The data center is where the data is. As we take you through this briefing you will see how Cisco pairs a full portfolio of AI/ML computing solutions with the simplicity and reach of cloud-managed infrastructure.”
- Why Cisco? To address this emerging opportunity and associated challenges, we are focusing on the full life cycle of an AI/ML project. First, we have experience in big data has helped many customers integrate changing data sources as part of a dynamic data pipeline. We have been helping customers extend their big data environment in AI/ML by purchasing UCS servers, populate with GPU’s, and connect to their data lake. We have consistently taken a no compromise approach to computing by developing highly available, richly configured systems, all based on a unified architecture, that can be seamlessly managed with existing infrastructure. Our existing UCS and HyperFlex systems have robust GPU support that can address all stages of an AI/ML project, so organizations can capitalize on the adaptability and programmability of the Cisco Unified Computing System and power AI workloads at scale. In a couple weeks, we will expand the UCS portfolio with a new optimized machine learning computing system targeted specifically on the model training stage. Will cover that system in more detail in a moment Second, we are bringing our experience from big data and the work we have done there with technology partners to bridge the gap to AI/ML. Using this proven approach, we are going to help IT demystify AI/ML in the data center with proven AI/ML computing solutions that combine a broad set of technologies and applications to help extract more intelligence out of all stages of the data life cycle while ensuring a faster, more reliable, and predictable deployment. Third, we can help prevent architectural silos, extend administrative expertise and simplify operations with a cloud managed computing system. With Cisco Intersight, we can make it easy to adopt new technologies anywhere, eliminating islands of standalone AI/ML servers, regardless of where they are located, in the data center, multi-site remote/branch, or the edge. One final point, in order to move the amount of data consumed by AI from its collection point to the models and back to where inferencing is taking place, who else than Cisco, the worldwide leader in networking technology, can provide the bandwidth and security required.
- With big data insight is gained and recommendations are provided through the analysis of the data. For example, Amazon recommends items to purchase based on your previous purchases and what other people purchased. With AI, it’s about machines making decisions based on the data. The data as it moves from one stage to the next, the data life cycle, requires work to prepare it for the next stage. So, in addition to validating popular machine learning stacks such as Kubeflow from Google enabling the creation of symmetric development and execution environments between on-premise and Google Cloud. We are also creating solutions that extend and integrate Hadoop with machine learning, making data located in data lakes accessible for the next stage of analytics. We feel it’s crucial for IT organizations, in order to have a faster path to success, to work with a vendor that has experience and track record of collaborating with a broad ecosystem and can bringing together the various components of AI in a more holistic solution. Problem: UCS Big Data Customers want deep learning to refine data into value Description: Hadoop loosely coupled with GPU nodes for deep learning with Jupyer notebook Solution: CVD Problem: For technical savvy data scientists to leverage Google DL capabilities on premise Description: Composable, portable, scalable ML Stack Enabling rapid development AND deployment Solution: CVD for UCS and HX Problem: How to better integrate Hadoop and DL? Description: Docker based application support YARN scheduling CPU & GPU HDFS with erasure encoding supporting tiered storage (1.7 copies instead of 3) Hot swap drives for HDFS
- Let’s reflect back to our challenges side where data volume, velocity, and variability is changing application behavior and placing new demands on infrastructure, unfamiliar software, and management of distributed and new technologies were outlined as key challenges. We have experience in big data has helped many customers integrate changing data sources as part of a dynamic data pipeline. We have been helping customers extend their big data environment in AI/ML by purchasing UCS servers, populate with GPU’s, and connect to their data lake. We have consistently taken a no compromise approach to computing by developing highly available, richly configured systems, all based on a unified architecture, that can be seamlessly managed with existing infrastructure. Our existing UCS and HyperFlex systems have robust GPU support that can address all stages of an AI/ML project, so organizations can capitalize on the adaptability and programmability of the Cisco Unified Computing System and power AI workloads at scale. In a couple weeks, we will expand the UCS portfolio with a new optimized machine learning computing system targeted specifically on the model training stage. Will cover that system in more detail in a moment Second, we are bringing our experience from big data and the work we have done there with technology partners to bridge the gap to AI/ML. Using this proven approach, we are going to help IT demystify AI/ML in the data center with proven AI/ML computing solutions that combine a broad set of technologies and applications to help extract more intelligence out of all stages of the data life cycle while ensuring a faster, more reliable, and predictable deployment. Third, we can help prevent architectural silos, extend administrative expertise and simplify operations with a cloud managed computing system. With Cisco Intersight, we can make it easy to adopt new technologies anywhere, eliminating islands of standalone AI/ML servers, regardless of where they are located, in the data center, multi-site remote/branch, or the edge. Lastly, in order to move the amount of data consumed by AI from its collection point to the models and back to where inferencing is taking place, who else than Cisco, the worldwide leader in networking technology, can provide the bandwidth and security required.