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1、Table of ContentsTable of Contents2Introduction4Critical Considerations for Businesses5Scalability5Flexibility5Cost Management5Compliance and Security6Key Components of AI Infrastructure6Data&Storage6Data Management6Data Sources6Data Preparation7Data Ingestion7RAG/Vector Databases7Data Governance8Da

2、ta Monitoring and Observability8Visualization and Analytics8Model Development&Training8Development Environment9Experiment Management9Orchestration9Model Testing and Fine-tuning10Deployment10Models10ML Models10LLMs10Multi-modal Models11Computer Vision Models11Model Hub11Monitoring&Observability11Obse

3、rvability12Monitoring12Model Endpoint Monitoring12Security&Compliance12RBAC13Compute13Hardware13Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.2Compute Options13Cluster Management14Resource Management14Multi-tenancy15Billing15Interoperability

4、15Base AI Frameworks16AI Inference Frameworks16ONNX(Open Neural Network Exchange)16Llama.cpp17vLLM17Triton17Introducing ClearML19How to Evaluate AI Infrastructure Solutions22Checklist for Evaluating AI Infrastructure Solutions23Summary and Next Steps24Next Steps24About ClearML24Copyright 2024 by Cle

5、arML.All rights reserved.All trademarks are the property of their respective owners.3IntroductionWe all know that AI has experienced explosive growth recently,driven by advancements in computing power,algorithms,and the vast amounts of data available.In turn,this growth has driven the need for sophi

6、sticated AIinfrastructure to support the building,training,deployment,and management of AI models as well as GenAIadoption at scale.According to research published by Allied Market Research and reported by CIO News,the trajectory of the GlobalAI Infrastructure Market reflects this burgeoning demand.

7、In fact,the report notes that the AI infrastructure marketwas valued at$23.5 billion in 2021 and is estimated to soar to an astounding$309.4 billion by 2031,growing at aCompound Annual Growth Rate(CAGR)of 29.8%from 2022 to 2031.One of the primary drivers of growth in the AI infrastructure market is

8、the realization among enterprises of how AIcan elevate their operational efficiency and enhance productivity,as well as expand revenue and reduce coststhrough the automation and orchestration of AI/ML workflows and workloads.For IT and AI Infrastructure professionals,understanding AI infrastructure

9、is crucial in deploying and managing AIapplications effectively.This guide aims to provide a comprehensive overview of the AI infrastructure market,highlighting the key components and critical considerations for businesses seeking to leverage AI technologieseffectively.In this guide,we will explore

10、the following key areas:Data&Storage:Efficient data storage solutions,such as data lakes and advanced databases,are essentialfor managing the large datasets that AI applications rely on.Model Development&Training:Advanced AI frameworks and tools facilitate the development and trainingof AI models,en

11、suring they are robust and capable of handling complex tasks.Models:AI models,built using machine learning and deep learning techniques,are the core componentsthat perform tasks such as prediction,classification,and pattern recognition.Monitoring&Observability:Continuous monitoring and observability

12、 of AI models and infrastructure arecrucial for maintaining performance,detecting anomalies,and ensuring reliability.Compute:High-performance computing resources,including GPUs and custom AI chips,are necessary tohandle the intensive computational demands of AI workloads.Interoperability:Ensuring in

13、teroperability between different AI systems and platforms is key to seamlessintegration and collaboration across the AI ecosystem.Other Considerations:Additional factors such as the ability to scale,security,compliance,andfuture-proofing all play significant roles in the overall effectiveness and su

14、stainability of your AIinfrastructure.Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.4Whether you are a business leader or an IT/AI infrastructure professional,this guide is designed to equip you withthe knowledge and insights needed to navig

15、ate the AI infrastructure landscape,so you can make informeddecisions and strategically invest in the AI infrastructure that will drive innovation and growth in your organization.Critical Considerations for BusinessesThe success of your AI initiatives hinges on a robust,adaptable,and efficient infra

16、structure.Thats why building afoundation that can seamlessly handle growth,integrate with diverse tools,manage costs,and ensure complianceand security is crucial.Here,we look at the four cornerstones of an effective AI infrastructure:Scalability,Flexibility,Cost Management,and Compliance and Securit

17、y.These principles will not only help you future-proof yourinvestments,but also drive operational efficiency,governance,and financial sustainability in your AI endeavors.ScalabilityScalability ensures your AI infrastructure can keep pace with increasing data volumes,AI workloads and risingcomputatio

18、nal demands,especially with the tidal wave of growing GenAI business use cases and rapid adoption.Todo that,you will want to seek solutions that offer seamless scaling capabilities and efficient resource managementto handle growth,safeguard investments through future-proofing,and enhance operational

19、 efficiency.Handling Growth:Ensures infrastructure can handle increasing data volumes and computational demands.Future-proofing:Safeguards investments by ensuring infrastructure can support evolving AI workloads.Operational Efficiency:Enhances operational efficiency by ensuring systems can scale dyn

20、amically withworkload demands.FlexibilityFlexibility is key to integrating various tools and frameworks,supporting hybrid environments,and adapting tochanging business needs.You will want an infrastructure that offers robust tool integration,supports hybridenvironments(on-premise and cloud),and adap

21、tability to changing business needs in order to stay resilient andcompetitive.Tool Integration:Supports integration with various tools and frameworks,enhancing operational flexibility.Hybrid Environments:Enables seamless operation across on-premise and cloud environments,supportingdiverse business n

22、eeds.Adaptability:Ensures infrastructure can adapt to changing business requirements,enhancing resilienceand competitiveness.Cost ManagementEffective cost management is crucial for sustainable AI operations.Consider solutions that provide comprehensivecost monitoring and control tools,optimize resou

23、rce and compute utilization,and maintain transparent billingpractices to ensure financial sustainability and budget control.Financial Sustainability:Effective cost management ensures AI operations are financially sustainable.Copyright 2024 by ClearML.All rights reserved.All trademarks are the proper

24、ty of their respective owners.5Resource Optimization:Maximize resource and compute utilization,reducing waste and improvingcost-efficiency.Budget Control:Provides tools for monitoring and controlling costs,ensuring financial discipline.Compliance and SecurityCompliance and security are non-negotiabl

25、e in AI infrastructure.Ensure your systems adhere to industryregulations and incorporate industry-standard security measures,including data encryption and role-based accesscontrol,to protect sensitive information and maintain trust and transparency.Regulatory Adherence:Ensures AI infrastructure comp

26、lies with industry regulations,avoiding legalpenalties.Data Protection:Protects sensitive data from breaches and unauthorized access,safeguarding businessintegrity.Trust and Transparency:Enhances trust and transparency by ensuring strong security measures andregulatory compliance.By prioritizing the

27、se elements,you can build an AI infrastructure that not only meets current demands but alsoevolves with your business,helping your organization drive long-term success and innovation.Next,well look at thedifferent elements of your AI infrastructure and their key considerations.Key Components of AI I

28、nfrastructureData&StorageEffective AI infrastructure is built on robust data management,which involves organizing,storing,and maintainingdata to ensure accessibility and reliability.Key components include:Data ManagementEffective data management is foundational for AI infrastructure,involving the or

29、ganization,storage,andmaintenance of data to ensure it is accessible and reliable.Key aspects include data architecture for optimizedperformance and scalability,and data lifecycle management to keep data accurate and relevant.This ensures thatAI models have high-quality data,essential for accurate a

30、nd reliable results.Key Capabilities and Considerations:Scalability:Can the system handle increasing amounts of data without performance degradation?Data Integrity:Are there mechanisms to ensure data accuracy and consistency?Backup and Recovery:How strong are the backup and recovery solutions?Copyri

31、ght 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.6Data SourcesIdentifying and integrating diverse data sources is crucial for a comprehensive AI system.These sources can includeinternal systems like databases and CRM,external data from APIs and publi

32、c datasets,and real-time data from IoTdevices.The ability to handle diverse data sources ensures a richer data pool,enhancing the insights andcapabilities of AI models.Key Capabilities and Considerations:Diversity:Can the system integrate various types of data(structured,unstructured,real-time)?Ease

33、 of Integration:How seamless is the integration process with existing systems?APIs and Connectivity:Are there comprehensive APIs and connectivity options for data sourcing?Data PreparationData preparation involves cleaning,transforming,and annotating data to make it suitable for analysis.Key activit

34、iesinclude pre-processing to remove noise and duplicates,creating pipelines for automated data processing tasks,andlabeling data for supervised learning models.Proper data preparation is critical for improving the quality andperformance of AI models.Key Capabilities and Considerations:Automation:How

35、 effectively can the system automate data cleaning and transformation tasks?Flexibility:Does it support various data formats and complex transformations?Labeling Efficiency:How user-friendly and scalable are the data labeling tools?Data IngestionEfficient data ingestion ensures that data is collecte

36、d and made available for analysis in a timely manner.Techniques include batch processing for loading large datasets periodically and stream processing for real-timedata ingestion.Efficient data ingestion is vital for maintaining up-to-date and actionable data for AI applications.Key Capabilities and

37、 Considerations:Real-time Processing:Can the system handle real-time data ingestion and processing?Batch Processing:Is batch processing efficient and reliable?Can it be scheduled and repeated to create aCI/CD process?Scalability:How well does the system scale with increasing data volumes and data up

38、dates and ongoingchange?RAG/Vector DatabasesRetrieval-augmented Generation(RAG)and vector databases are optimized for handling high-dimensional data andenabling fast retrieval and querying.These databases are crucial for applications that require quick access to largeand complex datasets,improving t

39、he speed and efficiency of AI model responses.Key Capabilities and Considerations:Performance:How fast is the data retrieval and querying?Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.7Scalability:Can the system handle large,high-dimensional

40、 datasets?Integration:How easily does it integrate with existing systems and workflows?Data GovernanceEnsuring data integrity and compliance through robust policies and procedures is essential.This includes adheringto regulatory standards like GDPR and CCPA,and maintaining detailed audit trails for

41、data access and changes.Strong data governance protects sensitive information and ensures legal compliance,building trust andtransparency.Key Capabilities and Considerations:Compliance:Does the system support compliance with regulatory standards(e.g.,GDPR,CCPA)?Policy Management:How robust is the po

42、licy management for data usage and access?Audit Trails:Are there detailed audit trails for data access and changes?Data Monitoring and ObservabilityContinuous monitoring and observability are necessary to maintain data quality and system performance.Thisinvolves tracking metrics and logs,setting up

43、alerts for anomalies,and using visualization tools to monitor data flowand performance.Effective monitoring ensures that any issues are quickly identified and addressed,maintainingthe reliability of AI systems.Key Capabilities and Considerations:Real-time Monitoring:Does the system provide real-time

44、 monitoring of data pipelines?Alerting:How effective are the alerting mechanisms for data quality issues?Visualization:Are there comprehensive visualization tools for data flow and performance?Visualization and AnalyticsEffective visualization tools and analytics platforms help in exploring data thr

45、ough interactive dashboards andextracting actionable insights using advanced analytics.Key considerations include the interactivity anduser-friendliness of visualization tools,support for advanced analytics,and integration with other data and analyticsplatforms.These tools are crucial for understand

46、ing and communicating data-driven insights,enhancingdecision-making and strategy formulation.Key Capabilities and Considerations:Interactivity:Are the visualization tools interactive and user-friendly?Advanced Analytics:Does the system support customized analytics?Integration:How well do the visuali

47、zation tools integrate with other data and analytics platforms?Model Development&TrainingEffective model deployment and training hinge on several crucial components that ensure efficiency,reproducibility,and optimal performance.These include:Copyright 2024 by ClearML.All rights reserved.All trademar

48、ks are the property of their respective owners.8Development EnvironmentA development environment is the interface through which AI builders actually build and test AI models.It spansIDEs,development containers,and hardware resources.This environment acts as a sandbox where AI projects canbe develope

49、d,experimented with,and optimized before moving to production.Key Capabilities and Considerations:Portability:Can the environment run on any infrastructure such as cloud,on-prem or hybrid?How difficultis it to run workloads on different infrastructures?How difficult is it to start work on one infras

50、tructure andthen shift to another?Containerization and Virtualization:Does the environment offer tools for creating isolated,replicableenvironments(e.g.,Containers,VMs or Python virtual environments)to ensure consistent experience acrossdifferent compute resources?Development Interface:Does the syst

51、em support your preferred tool(Interactive sessions such asnotebooks,or traditional IDEs such as VS Code and Pycharm)?Does the system support running these IDEsdirectly on your compute?Experiment ManagementExperiment Management involves meticulous tracking of model training experiments for reproduci

52、bility andoptimization through version control of datasets and models,and recording metadata like hyperparameters,execution environment,and experiment results.Key considerations include detailed tracking,reproducibility,andcollaborative capabilities.Key Capabilities and Considerations:Tracking:How d

53、etailed and comprehensive is the tracking of experiments?Reproducibility:Can experiments be easily and fully reproduced and validated?Collaboration:Does the system support collaboration among multiple users?OrchestrationOrchestration tools automate workflows,facilitating efficient model training and

54、 deployment by managing machinelearning pipelines and task scheduling.Essential features include robust automation,effective resource policing,scalability,and seamless integration with other tools.Key Capabilities and Considerations:Automation:How easy does the system make automating complex workflo

55、ws?Resource policing:What facilities does the system provide for managing resource allocation to differentconsumers?Scalability:Can the orchestration tools effectively handle the scale of resources required for model trainingand deployment?Integration:How easily does it integrate with complementing

56、tools and platforms?Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.9Model Testing and Fine-tuningEnsuring model accuracy and reliability requires rigorous testing and fine-tuning,supported by user-friendlyinterfaces and comprehensive performa

57、nce metrics.Integrating human feedback,such as through ReinforcementLearning with Human Feedback(RLHF),is crucial for enhancing model performance.Key Capabilities and Considerations:User Interface:Are the tools user-friendly and intuitive?Performance Metrics:How comprehensive are the performance met

58、rics for model evaluation?Feedback Integration:Can human feedback be easily integrated for reinforcement learning?DeploymentEfficient model deployment is critical for real-time applications,focusing on setting up infrastructure for servingpredictions,ensuring scalability to handle varying loads,opti

59、mizing for low-latency predictions,and maintainingreliability and uptime.Key Capabilities and Considerations:Scalability:How well does the deployment infrastructure scale with demand?Latency:Is the deployment optimized for low-latency predictions?Reliability:What mechanisms are in place for ensuring

60、 deployment reliability and uptime?ModelsAI infrastructure encompasses various model types,each with unique capabilities and applications.For example:ML ModelsTraditional machine learning models include supervised learning for labeled data,unsupervised learning for patterndiscovery in unlabeled data

61、,and reinforcement learning for trial-and-error learning.Key considerations for thesemodels are versatility in handling different types,performance on benchmark datasets,and ease of use for buildingand training.Key Capabilities and Considerations:Versatility:Can the platform handle various types of

62、machine learning models?Performance:How well do the models perform on benchmark datasets?Ease of Use:How user-friendly are the tools for building and training models?LLMsLarge Language Models like GPT-4o or Llama 3.1 excel in natural language processing and contextualunderstanding,handling complex q

63、ueries with advanced capabilities.Important factors include how well the modelhandles context,its adaptability for specific tasks,and scalability for large-scale applications.Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.10Key Capabilities a

64、nd Considerations:Contextual Understanding:How well does the model handle complex queries and context?Adaptability:Can the model be fine-tuned for specific tasks and domains?Scalability:How scalable is the model for large-scale applications?Multi-modal ModelsThese models integrate multiple data type

65、s(text,image,audio)for cross-domain learning and enhanced taskperformance.Key considerations are the seamless integration of different data types,performance acrossmodalities,and flexibility for adapting to new tasks and data types.Key Capabilities and Considerations:Integration:How seamlessly can d

66、ifferent data types be integrated and processed?Performance:How well do the models perform across various modalities?Flexibility:How easily can the models be adapted for new tasks and data types?Computer Vision ModelsThese models enable computers to interpret and understand visual information from t

67、he world,such as imagesand videos,and are trained to perform a variety of tasks(such as object detection or image classification,amongothers)by learning patterns and identifying features from large datasets.Key Capabilities and Considerations:Accuracy:How accurate is the model in identifying and cla

68、ssifying objects within images and videos?Real-Time Processing:Can the model process and analyze visual data at a pace that meets your specificrequirements(such as strict FPS-frames per second-requirements)?Generalization:How well does the model generalize across diverse and unseen data,maintainingp

69、erformance in different environments and conditions?Model HubPlatforms like Hugging Face offer access to pre-trained models and community contributions,providing a variety ofready-to-use models.Essential aspects include the range and reliability of available models and the strength ofcommunity suppo

70、rt and contributions.Key Capabilities and Considerations:Variety:Is there a wide range of pre-trained models available?Quality:How reliable and well-maintained are the models in the hub?Community Support:Is there strong community support and contributions?Copyright 2024 by ClearML.All rights reserve

71、d.All trademarks are the property of their respective owners.11Monitoring&ObservabilityMonitoring and observability in AI infrastructure are essential for ensuring system performance,reliability,security,and compliance,while enabling early issue detection and resource optimization.Components include

72、:ObservabilityVisibility into AI system operations is crucial.This includes capturing detailed logs of system activities(logging)andtracking data flow and requests(tracing).Key considerations include the comprehensiveness of visibility,the abilityto drill down into detailed metrics and logs,and seam

73、less integration with existing systems.Key Capabilities and Considerations:Visibility:How comprehensive is the visibility into system operations?Granularity:Can you drill down into detailed metrics and logs?Integration:How well does the observability tool integrate with existing systems?MonitoringCo

74、ntinuous monitoring of hardware and serving infrastructure is essential.This involves tracking performancemetrics(CPU,GPU,memory usage)and conducting regular health checks.Important capabilities include real-timemonitoring,effective alerting mechanisms for performance issues,and customizable,user-fr

75、iendly dashboards.Key Capabilities and Considerations:Real-time Monitoring:Does the system provide real-time monitoring capabilities?Alerting:Are there effective alerting mechanisms for performance issues?Dashboarding:Are the dashboards customizable and user-friendly?Model Endpoint MonitoringEnsurin

76、g the reliability of deployed model endpoints involves measuring response times and request handlingcapacity(latency and throughput)and tracking error rates.Key considerations are the effectiveness of latency andthroughput monitoring,robust error tracking,and the systems scalability to handle numero

77、us endpoints.Key Capabilities and Considerations:Latency and Throughput:How well are latency and throughput monitored?Error Tracking:How effective is the tracking and reporting of errors?Visibility:Do you have visibility over which models are being served,and where,to ensure data security?Scalabilit

78、y:Can the monitoring system handle a large number of endpoints?Security&ComplianceMaintaining security and compliance is vital.This includes data encryption to protect data at rest and in transit andimplementing Role-Based Access Control(RBAC)for managing permissions.Key capabilities include robuste

79、ncryption methods,granular and flexible access controls,and support for compliance with industry regulations.Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.12Key Capabilities and Considerations:Encryption:Are robust encryption methods used fo

80、r data at rest and in transit?Access Controls:How granular and flexible are the access control mechanisms?Compliance:Does the system support compliance with industry regulations?RBACRole-Based Access Control ensures secure and efficient management of access privileges.This involves definingroles bas

81、ed on responsibilities and managing permissions accordingly.Important considerations include theflexibility of role definition,an intuitive and comprehensive permission management interface,and detailed auditlogs for access and changes.Key Capabilities and Considerations:Role Definition:How flexible

82、 is the system in defining roles?Permission Management:How intuitive and comprehensive is the permission management interface?Audit Trails:Are there detailed audit logs for access and changes?ComputeAccording to ClearMLs recent research report,“The State of AI Infrastructure at Scale 2024,”96%of com

83、panies planto expand their AI compute capacity and investment with availability,cost,and infrastructure challenges weighingon their minds.Nearly all respondents(96%)plan to expand their AI compute infrastructure,with 40%consideringmore on-premise and 60%considering more cloud,and they are looking fo

84、r flexibility and speed.The top concernfor cloud compute is wastage and idle costs.Lets take a closer look.HardwareFuture-proofing your infrastructure means ensuring compatibility with various hardware platforms(hardware-agnostic),different types of silicon chips(silicon-agnostic),multiple cloud pro

85、viders(cloud-agnostic),andavoiding vendor lock-in(vendor-agnostic).Key Capabilities and Considerations:Future-proofing:Is the hardware infrastructure easily extensible to support future AI workloads?Compatibility:Is the hardware infrastructure compatible with various AI frameworks and tools?Scalabil

86、ity:Can the hardware infrastructure be easily scaled?Compute OptionsBuilding the right compute infrastructure involves balancing on-premise and cloud solutions,utilizing GPUresources on demand(GPU-as-a-Service),simplifying AI model deployment and scaling(ServerlessGPU/Inference-as-a-Service),and sup

87、porting low-latency edge computing.Respondents to ClearMLs AIInfrastructure survey were fairly evenly divided between their current infrastructure setup.33%have compute fullyon-premise,38%are fully cloud,and a little less than 29%have hybrid environments of both on-prem and cloud.Copyright 2024 by C

88、learML.All rights reserved.All trademarks are the property of their respective owners.13Key Capabilities and Considerations:On-prem/Cloud:Does the solution offer flexibility between on-premise and cloud options?GPU-as-a-Service:How accessible and cost-effective are GPU resources?Serverless GPU:Are t

89、here options for serverless deployment to simplify scaling?Edge Computing:Does the system support low-latency edge computing?Cluster ManagementEfficient cluster management ensures optimal resource utilization through effective resource allocation based onworkload demands and scalability.Key Capabili

90、ties and Considerations:Resource Allocation:How efficiently does the system allocate resources?Scalability:Can the cluster management system handle dynamic scaling?Ease of Use:Is the interface user-friendly for managing clusters?Resource ManagementEffective resource management includes monitoring re

91、source usage(Resource Dashboard),scheduling tasks(AI/HPC Workload Scheduler),defining resource distribution policies(Resource Allocation Policy Management),andmanaging budgets and costs,including cloud spillover and autoscalers.For example:Resource Dashboard:Comprehensive dashboards provide visibili

92、ty into resource usage and availability,facilitating informed decision-making.AI/HPC Workload Scheduler:Optimizes task scheduling to maximize resource efficiency and reduce idletimes.Resource Allocation Policy Management:Defines and enforces policies for resource distribution,ensuringfair and effici

93、ent usage.Policies/Control:Implements rules for managing resources effectively,preventing resource contention andensuring optimal performance.Budgets/Cost Management:Robust tools for monitoring and controlling costs are crucial for financialsustainability.40%of respondents to ClearMLs AI Infrastruct

94、ure survey,regardless of company size,said they were planning touse orchestration and scheduling technology to maximize their existing compute infrastructure.When asked about peak periods for GPU usage,15%of respondents report that less than 50%of their available andpurchased GPUs are in use.53%beli

95、eve 51-70%of GPU resources are utilized,and just 25%believe their GPUutilization reaches 85%.Only 7%of companies believe their GPU infrastructure achieves more than 85%utilizationduring peak periods.When asked about current methods employed for managing GPU usage,respondents are employing queuemanag

96、ement and job scheduling(67%),multi-instance GPUs(39%),and quotas(34%).Methods of optimizing GPUCopyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.14allocation between users include Open Source solutions(24%),HPC solutions(27%),and vendor-specifi

97、c solutions(34%).Another 11%use Excel and 5%have a home-grown solution.Only 1%of respondents do not maximize oroptimize their GPU utilization.Key Capabilities and Considerations:Dashboard:How comprehensive and customizable is the resource dashboard?Scheduler:Does the scheduler optimize resource usag

98、e effectively?Cost Management:Are there robust tools for monitoring and controlling costs?Multi-tenancyIn computing,multi-tenancy is a software architecture where a single instance of an application(or in this case,acomputing resource)serves multiple individual user groups,often referred to as tenan

99、ts.It offers a way to shareresources efficiently amongst those users.In addition to its expanded capability that satisfies more AI stakeholders,a multi-tenant structure allows infrastructure leaders to operate more efficiently through dynamic resourcemanagement.Key Capabilities and Considerations:Re

100、source Isolation and Optimization:How does the multi-tenancy model ensure that resources areisolated and optimized for each tenant to maintain stable performance and minimize operational costs?Security and Data Privacy:What mechanisms does the multi-tenancy infrastructure provide to ensurerobust dat

101、a security and tenant isolation,while meeting regulatory compliance and access controlrequirements?Cost Efficiency and Scalability:How does multi-tenancy enable cost-effective use of shared infrastructureand ensure scalability to meet varying demands without incurring unnecessary expenses?BillingWhe

102、n implementing multi-tenant billing in AI infrastructure,its important to focus on capabilities that ensureaccurate,transparent,and scalable billing processes.Here are the top three capabilities to consider:Key Capabilities and Considerations:Granular Usage Tracking:How does the billing system ensur

103、e accurate and real-time tracking of resourceusage at a granular level for each tenant?Flexible Pricing Models:Does the billing system support customizable pricing models and the ability toapply discounts or promotions to meet diverse tenant needs?Transparent Reporting and Invoicing:How does the bil

104、ling system provide clear,detailed,and automatedinvoices that scale with the growing number of tenants?InteroperabilityInteroperability is a key consideration in choosing the right technology to develop your AI solution.Training modelson a beefy machine is fine(and fast),but AI builders must keep in

105、 mind the target platform on which the model willbe used for inference.For that,having a silicon-agnostic solution is important.Thats because eventually,if yourCopyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.15network cant run on the target co

106、mpute(or even multiple computes),its only a job half done.Tools andframeworks are available to ensure interoperability,such as ONNX as an interoperability framework and inferenceengines such as vLLM and llama.cpp.Base AI FrameworksThese are frameworks for AI builders,building their own models or lev

107、eraging existing codebases.They offer a lot offlexibility,but have higher barriers of entry.Popular frameworks have large user bases,with an active communityand development roadmaps.They are use-case agnostic and have built-in support for hardware accelerators.Thetwo most popular frameworks are Tens

108、orFlow and PyTorch,but others are still in use.Key Capabilities and Considerations:Compatibility:Are there codebases or pre-built models for your specific use case already written in thechosen framework?Leveraging existing resources can shorten time to market and reduce developmenteffort.Community:H

109、ow active and supportive is the community in providing resources,troubleshooting,andcontributing innovations?A strong community ensures the framework remains up-to-date,reliable,andwell-documented.Ease of Use and Flexibility:How intuitive is the framework for building,debugging,and deploying models?

110、A user-friendly interface and flexibility in adapting to new needs can significantly speed up experimentationand development.AI Inference FrameworksAdvanced AI frameworks offer specialized capabilities that enhance model development and deployment.ONNX(Open Neural Network Exchange)What It Is:A frame

111、work that facilitates model interoperability across different AI tools and platforms.Importance:ONNX serves as a bridge between model builders and inference engines,streamlining the deploymentof models across diverse hardware and inference platforms.By standardizing models into a common format,ONNXa

112、llows model builders to focus on porting their models to ONNX itself,eliminating the need to adapt to differenthardware platforms or inference engines.This broad support for accelerated hardware simplifies deployment,ensuring models run efficiently across various configurations while saving time and

113、 reducing complexity for modelbuilders.Key Capabilities and Considerations:Interoperability:How good is the support for model interoperability in integrating different AI tools andframeworks for seamless collaboration and innovation?Scalability:How well are the AI frameworks optimized for scalabilit

114、y to handle the increasing complexityand volume of data in modern AI applications?Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.16Advanced Features:What advanced features do the AI frameworks offer,such as automated modeloptimization,efficie

115、nt resource management,and support for cutting-edge research,to enable developersto push the boundaries of what AI can achieve?Llama.cppWhat It Is:An optimized inference engine written in CPP with built in hardware accelerator and broad neuralnetwork supportImportance:A portable inference engine tha

116、t makes LLMs easily accessible via HTTP endpoints.It supportshardware acceleration for both CPUs and GPUs,along with CPU offloading.llama.cpp is compatible with popularmodels such as LLaMA,Mistral,and more.It includes a built-in web server for quickly deploying OpenAIAPI-compatible endpoints.By usin

117、g engines like llama.cpp,AI developers can expose their models without needingto write additional code,streamlining the deployment process.Key Capabilities and Considerations:High Performance:High model throughput with hardware acceleration.Ease of Use:Ability to simply set up an open AI API compati

118、ble endpointModel Support:Supports most state of the arts LLMsvLLMWhat It Is:vLLM is an inference engine specifically designed to optimize the inference process for large languagemodels(LLMs).Importance:vLLM makes the task of running an LLM model for inference simple.It optimizes model inference wit

119、hefficient memory management and hardware acceleration(Such as with Cuda)as well as standard feature such asquantization.It is open-source and can run on chips from various silicon vendors(Such as GPUs from Nvidia andAMD,CPUs by Intel and AMD,TPU and AWS Neuron chips).It supports popular model forma

120、ts.It supports bothbatch inference as well as setting up HTTP endpoints.Key Capabilities and Considerations:Optimized Execution:vLLM optimizes memory and computation for improved performance.This results infaster response times and reduced latency.Interoperability:vLLM works on chips from multiple s

121、ilicon vendors,in the cloud or on-prem.Scalability:vLLM scales efficiently across multiple systems,enabling it to handle large workloads and servemany users without performance degradation.TritonWhat It Is:Triton is an open-source inference serving platform that enables the deployment and management

122、 ofmultiple AI models across various frameworks in production environments.It is developed by NvidiaImportance:Nvidia Triton simplifies and optimizes the deployment of AI models in production environments bysupporting multiple frameworks(such as TensorFlow,PyTorch,ONNX,and TensorRT)and various hardw

123、areCopyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.17backends such as GPUs and CPUs.Triton offers advanced features like dynamic batching,model versioning,andmulti-model support,ensuring scalability and high throughput for AI applications.Its

124、support for variousframeworks,with advanced features makes it ideal for serving models over Nvidia GPUs and CPUs.Key Capabilities and Considerations:Multi-Framework Support:Triton can serve models from multiple machine and deep learning frameworks,such as TensorFlow,PyTorch,ONNX,and TensorRT,allowin

125、g flexibility in model deployment across differentAI tools.Inference Optimization:Triton optimizes inference performance by dynamically batching incomingrequests,reducing latency,and maximizing hardware utilization without needing manual intervention.Scalability and Multi-Model Support:Triton enable

126、s the serving of multiple models simultaneously,making it scalable for production environments with varying workloads and demand,ensuring efficientresource management.Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.18Introducing ClearMLClearML

127、s end-to-end AI Platform is designed to streamline AI adoption and the entire AI development lifecycle.Thisunified,open source platform supports every phase of AI development,from lab to production,allowingorganizations to leverage any model,dataset,or architecture at scale.ClearMLs platform integra

128、tes seamlessly withexisting tools,frameworks,and infrastructures,offering unmatched flexibility and control for AI builders,as well asIT and DevOps teams building,training,and deploying models at every scale on any AI infrastructure.ClearML is known as the most flexible,wholly agnostic,end-to-end AI

129、 platform in the marketplace today,in that it is:Silicon-agnostic:supporting NVIDIA,AMD,Intel,ARM,and other GPUsCloud-agnostic:supporting Azure,AWS,GCP,Genesis Cloud,and others,as well as multi-cloud andon-prem workloads.Vendor-agnostic:supporting the most popular AI and machine learning frameworks,

130、libraries,and tools,such as PyTorch,Keras,Jupyter Notebooks,and others.Completely modular:Customers can use the full platform alone or integrate it with their existing AI/MLframeworks and tools such as Grafana,Slurm,MLflow,Sagemaker,and others to address GenAI,LLMOps,and MLOps use cases and to capit

131、alize on existing investments.Our platform encompasses newly expanded capabilities and integrates previous stand-alone products,andincludes:A GenAI App Engine,designed to make it easy for AI teams to build and deploy GenAI applications,maximizing the potential and the value of their LLMs.An Open Sou

132、rce AI Development Center,which offers collaborative experiment management,powerfulorchestration,easy-to-build data stores,and one-click model deployment.Users can develop their ML codeand automation with ease,ensuring their work is reproducible and scalable.An AI Infrastructure Control Plane,design

133、ed as a universal operating system for AI infrastructure,helpingcustomers manage,orchestrate,and schedule GPU compute resources effortlessly,whether on-premise,inthe cloud,or in hybrid environments.The Control Plane maximizes GPU utilization and provides fractionalGPUs,as well as multi-tenancy and e

134、xtensive billing and chargeback capabilities while providing precise costcontrol,empowering customers to optimize their compute resources efficiently.ClearML is available as a unified,open source platform or a modular offering as part of your existing full-stacksolution for AI.Use all of our modules

135、 for a complete end-to-end ecosystem,or swap out any module with tools youalready have for a custom experience.No more pricey walled gardens,vendor lock-ins,or fragmented pointsolutions.Explore ClearMLs end-to-end AI Platform and see why were the right choice for your AI use case:Copyright 2024 by C

136、learML.All rights reserved.All trademarks are the property of their respective owners.19Figure 1:ClearMLs End-to-End AI PlatformOur AI Platform enables customers to use any type of machine learning,deep learning,or large language model(LLM)with any dataset,in any architecture,at scale.AI Builders ca

137、n seamlessly develop their ML code andautomation,ensuring their work is reproducible and scalable.Thats important,because it addresses several criticalchallenges faced by organizations in developing,deploying,and managing AI solutions building AI in the mostcomplex and demanding environments.Heres w

138、hy it matters:Unified End-to-end Workflow:ClearML provides a seamless workflow that integrates all stages of AIdevelopment,from data ingestion and model training to deployment and monitoring.This unified approacheliminates the need for multiple disjointed tools,simplifying the AI adoption and develo

139、pment process.Superior Efficiency&ROI:ClearMLs new AI infrastructure orchestration and management capabilities helpcustomers execute 10X more AI and HPC workloads on their existing Infrastructure.Interoperability:The platform is designed to work with any machine learning framework,dataset,orinfrastr

140、ucture,whether on-premise,in the cloud,or in a hybrid environment.This flexibility ensures thatorganizations can use their preferred tools and avoid vendor lock-in.Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.20Orchestration&Automation:Clea

141、rML automates many aspects of AI development,such as datapreprocessing,model training,and pipeline management.This ensures full utilization of compute resourcesfor multi-instance GPUs and job scheduling,prioritization,and quotas.ClearML empowers team membersto schedule resources on their own with a

142、simple and unified interface,enabling them to self-serve withmore automation and greater reproducibility.Scalable Solution:The platform supports scalable compute resources,enabling organizations to handlelarge datasets and complex models efficiently.This scalability is crucial for keeping up with th

143、e growingdemands of AI applications.Optimized Resource Utilization:By providing detailed insights and controls over compute resourceallocation,ClearML helps organizations maximize their GPU and cloud resource utilization.This optimizationleads to significant cost savings and prevents resource wastag

144、e.Budget&Policy Control:ClearML offers tools for managing cloud compute budgets,including autoscalersand spillover features.These tools help organizations predict and control their monthly cloud expenses,ensuring cost-effectiveness,by providing advanced user management for superior quota/over-quotam

145、anagement,priority,and granular control of compute resources allocation policies.Enterprise-Grade Security:The platform includes robust security features such as role-based accesscontrol,SSO authentication,and LDAP integration.These features ensure that data,models,and computeresources are securely

146、managed and accessible only to authorized users.Real-Time Collaboration:The platform facilitates real-time collaboration among team members,allowingthem to share data,models,and insights effectively.This collaborative environment fosters innovation andaccelerates the development process.Multi-tenanc

147、y:ClearML supports secure multi-tenancy,so you can easily set up compute infrastructure tobe shared securely amongst multiple tenants.Our enterprise-grade security capabilities(SSOauthentication,LDAP integration,and role-based access control)combined with detailed reporting oncurrent user usage,make

148、 it simple to manage multiple accounts with detailed usage information foraccurate billing.Billing&Chargeback Capabilities:ClearML enables Cloud Service Providers(CSPs)as well as largeEnterprises to invoice based on granular usage data for accurate margin capture.With ClearML,CSPs/Enterprises gain v

149、isibility and complete control over the amount of computing time,data storage,APIcalls,or microservices consumed by each account.Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.21How to Evaluate AI Infrastructure SolutionsAs a technical profes

150、sional,you play a crucial role in evaluating your organizations AI infrastructure solutions andservices to ensure they align with your organizations needs for efficiency,scalability,and cost-effectiveness.Yourevaluation process should involve a combination of technical assessment,strategic planning,

151、and operationalconsiderations to select the infrastructure that best supports AI and machine learning(ML)workloads.Heres anoverview of how you might approach this task:1)Define Requirements:Your first step is to define the specific requirements of your AI projects,includingthe types of AI/ML workloa

152、ds,the expected volume of data processing,and the computational demands.This helps you narrow down the options to those solutions that can adequately meet your projects needs.2)Assess Scalability and Performance:You evaluate the scalability of AI infrastructure solutions to ensurethey can efficientl

153、y handle varying workloads and data volumes as AI applications grow.Performancemetrics,such as processing speed and latency,are also crucial,especially for time-sensitive AI tasks thatrequire real-time analysis.3)Consider Cost-Efficiency:Cost is a significant factor in your selection process.You ana

154、lyze the total cost ofownership,which includes not only the upfront costs but also ongoing operational expenses such asmaintenance,scaling,and energy consumption.You look for solutions that offer the best balance betweencost and performance,taking advantage of features like dynamic resource allocati

155、on and auto-scaling tooptimize expenses.4)Evaluate Integration Capabilities:Integration with existing tools,platforms,and workflows is essential tomaintain smooth operations.You assess the compatibility of AI infrastructure solutions with yourorganizations current environment,including development t

156、ools,data storage systems,and operationalmonitoring solutions.Your goal is to ensure a seamless integration that minimizes disruption andmaximizes productivity.5)Check for Flexibility and Ease of Use:The selected AI infrastructure should be flexible enough to supportvarious AI frameworks.Ease of use

157、 is also critical,as it affects the efficiency of development and operationalteams.You prefer solutions that offer user-friendly interfaces,comprehensive documentation,and strongcommunity support.6)Review Security and Compliance:Security features and compliance with regulatory standards arenon-negot

158、iable criteria.You thoroughly review the security measures provided by the AI infrastructuresolutions,including data encryption,access controls,and audit trails.Compliance with industry standardsand regulations ensures that your organization meets legal and ethical obligations.7)Analyze Vendor Suppo

159、rt and Community:Strong vendor support can significantly impact the successfuldeployment and ongoing management of AI infrastructure.You evaluate the level of support offered by theCopyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.22vendor,inclu

160、ding technical assistance,training,and resources for troubleshooting.A vibrant and activecommunity around the solution can also provide additional support and insights.8)Perform Proof of Concept(PoC):Before making a final decision,you often conduct a Proof of Concept(PoC)to test the AI infrastructur

161、e solutions in a controlled environment.This allows you to assess firsthandhow well the solution meets your requirements,how it integrates with existing systems,and its ease of useand performance under realistic workloads.Checklist for Evaluating AI Infrastructure SolutionsIf you are evaluating AI I

162、nfrastructure solutions,either for PoC or purchase,here is a handy checklist to see if yoursolution has the features and functionality you need:Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.23Feature/FunctionalityClearMLHow Does Your Solutio

163、n Compare?Data&StorageModel Development&TrainingModelsMonitoring&ObservabilityComputeInteroperabilitySummary and Next StepsNext StepsGet started with ClearML by using our free tier servers or by hosting your own.Read our documentation here.Youll find more in-depth tutorials about ClearML on our YouT

164、ube channel.We also have a very active Slack channel for anyone who needs help.Read how our customers have been successful in using ClearML as their platform for continuous AI andmachine learning.If you need to unleash AI at scale within your organization,please request a demo.About ClearMLAs the le

165、ading open source,end-to-end solution for unleashing AI in the enterprise,ClearML is used by more than1,600 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle,fromproduct feature exploration to model deployment and monitoring in production.Use all of

166、our modules for acomplete ecosystem or plug in and play with the tools you have.ClearML is an NVIDIA Partner and is trusted bymore than 250,000 forward-thinking AI builders and IT teams at leading Fortune 500 companies,enterprises,academia,public sector agencies,and innovative start-ups worldwide.To learn more,visit the companys website athttps:/clear.ml.Copyright 2024 by ClearML.All rights reserved.All trademarks are the property of their respective owners.24