Enterprise MLOps in the Cloud

更快地工作，更快地部署结果，快速扩展，并降低监管和操作风险

Domino为跨组织的数据科学活动提供了一个中央记录系统. Domino编排所有数据科学构件，包括AWS基础设施、数据和服务.

Code-first data science teams benefit from a flexible, collaborative, and reproducible research environment, 在IT治理范围内自助访问强大的AWS基础设施.

Unleash Data Science in the Cloud

Enterprise MLOps on AWS

Domino的企业MLOps平台运行在Amazon Elastic Kubernetes Service (EKS)上。. Domino是AWS高级技术永利贵宾会，具有机器学习和金融服务能力.

Self-serve Infrastructure

使用支持Amazon S3的预构建数据连接器，为数据科学家消除常见的DevOps障碍, Redshift, EMR, and others. 自助访问Amazon EC2机器(包括gpu和功能强大的服务器).

Deploy Models Anywhere

在Amazon SageMaker中部署Domino模型，以实现来自AWS的可伸缩性和低延迟托管. 在Domino中部署SageMaker模型，以监视跨工具和团队的模型性能.

Built for Data Science Teams

提供跨数据科学团队的项目管理和协作，同时支持工具(Jupyter, R, SAS, MATLAB), packages, and compute frameworks (Spark, Ray, Dask, MPI) of choice. Compound knowledge instead of reinventing the wheel.

Demonstrated Expertise for ML Solutions on AWS

永利贵宾会 is a founding member of the AWS Machine Learning Competency Partners, validated since 2017. AWS认为Domino是帮助组织从数据科学家投资中获取真正价值的关键创新者之一.

永利贵宾会 is also an AWS Financial Services Competency Partner 这是因为达美乐在银行、保险和算法交易领域的影响力很大.

Collaborative, flexible model development and deployment for teams

为数据科学家编排AWS基础设施、数据和服务.

Host on AWS
AWS-Certified
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Pre-built Connectors
Seamless AWS Data Access
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Self-Serve Infrastructure
Automate DevOps
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Models Anywhere
Flexible Deployment
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Enterprise MLOps Deployed on AWS

自2017年以来，Domino是AWS机器学习能力永利贵宾会. Domino在Amazon EKS上的企业MLOps平台提供了随时可部署的功能, field-tested deployment patterns for ease of management. Domino可以在您选择的AWS区域(包括GovCloud)中的专用VPC中运行.

Kubernetes为容器化应用程序带来了一系列全新的好处, including efficient hardware/compute utilization. k8能够在容量达到峰值时自动向上扩展，并在达到峰值后再次向下扩展.

向上或向下扩展集群既快速又容易，因为这只是向集群添加或删除虚拟机(vm)的问题. This dynamic resource allocation is especially beneficial for data science workloads; the demand for high-powered CPUs, 当训练模型或工程特征时，gpu和RAM可能非常密集, but then the demand can scale down again very quickly.

Domino Documentation: How to Deploy Domino on Amazon EKS
Domino 4.3 Blog: Domino Support for Amazon EKS

Seamless Access to Amazon Redshift and Amazon S3

Domino Data Sources 提供一种机制来创建和管理受支持的外部数据服务的连接属性, such as Amazon Redshift, Amazon S3, and others. 连接属性是安全存储的，不需要安装特定于数据源的驱动程序和库.

Domino中的数据源通过消除与驱动程序安装相关的DevOps障碍，使数据访问民主化, specific libraries, and more. 通过与同事共享数据源连接器，可以增强团队协作. IT teams will be pleased that the capability s支持每个用户和服务帐户凭据，以最大限度地提高灵活性，同时保持最高级别的数据安全性.

Flexible Tooling and Self-Serve Infrastructure - Governed by IT

Domino的协作和灵活的平台允许数据科学家使用他们选择的工具和软件包, including Amazon SageMaker, Jupyter, RStudio, SAS, Anaconda, MATLAB, along with flexible compute frameworks (i.e., Spark, Ray, and Dask). 代码优先的数据科学家从项目管理和协作中获益, 具有灵活部署选项的可重复研究环境-通过复合知识最大化生产力和业务影响.

抽象出管理基础设施和连接数据源的复杂性, so data scientists can focus on innovation. Provide self-serve, easy access to Amazon EC2 machines, including GPUs and powerful servers, 更快地运行实验并测试更多的想法以加速模型开发.

Self-Service Infrastructure Portal Product Page: Domino自动执行大规模数据科学工作所需的耗时的DevOps任务.
NVIDIA GPUs in AWS:由NVIDIA gpu支持的Amazon EC2实例提供快速机器学习训练所需的可扩展性能, cost-effective ML inference, flexible remote virtual workstations, and powerful HPC computations.
Data Science Blog: Spark, Dask, and Ray: Choosing the Right Framework.

Domino-Model-Monitor-AWS-Solution-Architecture-12142020

Deploy and Monitor Models Anywhere

Models developed in Domino can be exported for deployment in Amazon SageMaker，让客户可以选择AWS自己的可扩展和低延迟主机. Models developed in 可以在Domino内部访问Amazon SageMaker和Amazon SageMaker Autopilot to support diverse business and operational requirements, 然后在预测失去准确性之前监测漂移和预测性能问题.

Amazon SageMaker Services/Features Complimentary to 永利贵宾会:

SageMaker Data Wrangler
SageMaker Batch Transform
SageMaker Data Labeling
SageMaker AutoPilot

SageMaker Neo
SageMaker Edge Manager
SageMaker Feature Store
SageMaker Clarify

SageMaker Serverless Inference
SageMaker Asynchronous Interface

More Resources

On-Demand Webinar:企业的模型管理和监控(在Domino中部署SageMaker Autopilot模型).
AWS APN Blog: How to Export a Model from Domino for Deployment in SageMaker.
Github Project: How to Architect end-to-end development, monitoring, and maintenance of your models in AWS and 永利贵宾会.