上海现场MLOps培训

上海现场MLOps培训

在上海由讲师进行实时指导的MLOps本地培训课程。

上海 - 六八八广场

上海六八八广场

上海, Shanghai 南京西路688号 ,
Shanghai, Henderson 688
在上海的培训中心学习MLOps。六八八广场位于国际甲级写字楼的第十六层,是一座现代化发展的中心,在繁华的南京西路,繁华的中心商务区。南京西路是上海最著名的商业区,有高档的商场,办公大楼和上海展览中心。688广场是一个靠近人民广场的标志性建筑,为国际公司提供一流的工作环境、居住环境和美食。在其附近有五星级的酒店如万豪酒店、最佳西方四季酒店、公园酒店等。 可步行直达地铁2号线。只需25分钟的车程,即可到达虹桥国际机场和高速铁路站,60分钟到浦东国际机场。 查看更多

上海 - 上海中区广场

中区广场2层

上海, Shanghai 黄浦区黄陂北路227号 200003 ,
上海 - 上海中区广场
在上海的培训中心学习MLOps。上海中区广场位于人民广场西侧的黄陂北路,被众多上海著名地标性建筑所环绕,像市政府大楼、上海会展中心、上海博物馆等都在附近,十分方便。我们的办公空间就在大楼的第2层。上海中区广场周边有很多五星级酒店,例如:艾美酒店、雷迪森酒店、JW万豪酒店等,还有很多便民设施,例如:各式美食餐厅、大型购物中心、金融服务机构等。 上海中区广场步行几分钟即可到达上海最大也是最繁忙的交通枢纽——地铁人民广场站,它是地铁1、2和8号线的交叉换乘站。而此地的人民广场是上海最著名的行政、商业和文化中心,平时也都是车水马龙,商业气息浓郁。从上海中区广场开车到上海火车站仅需15分钟,如果到虹桥国际机场或高铁火车站也只需25分钟车程。 雷格斯上海中区广场办公空间为上海本地和其它国内外公司提供了一个最完美的办公解决方案,并以最合理的价格为公司提供最周到的综合服务、最齐全的办公设备、最灵活的办公空间。 查看更多

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MLOps子类别

上海现场MLOps课程大纲

课程名称
课程时长
课程概览
课程名称
课程时长
课程概览
35小时
课程概览
This instructor-led, live training in 上海 (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.

By the end of this training, participants will be able to:

- Install and configure various MLOps frameworks and tools.
- Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
- Prepare, validate and version data for use by ML models.
- Understand the components of an ML Pipeline and the tools needed to build one.
- Experiment with different machine learning frameworks and servers for deploying to production.
- Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
28小时
课程概览
This instructor-led, live training in 上海 (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.

By the end of this training, participants will be able to:

- Install and configure Kubernetes, Kubeflow and other needed software on AWS.
- Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.
- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
- Leverage other AWS managed services to extend an ML application.
28小时
课程概览
This instructor-led, live training in 上海 (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud.

By the end of this training, participants will be able to:

- Install and configure Kubernetes, Kubeflow and other needed software on Azure.
- Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
- Leverage other AWS managed services to extend an ML application.
28小时
课程概览
This instructor-led, live training in 上海 (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Google Cloud Platform (GCP).

By the end of this training, participants will be able to:

- Install and configure Kubernetes, Kubeflow and other needed software on GCP and GKE.
- Use GKE (Kubernetes Kubernetes Engine) to simplify the work of initializing a Kubernetes cluster on GCP.
- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
- Leverage other GCP services to extend an ML application.
28小时
课程概览
This instructor-led, live training in 上海 (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to IBM Cloud Kubernetes Service (IKS).

By the end of this training, participants will be able to:

- Install and configure Kubernetes, Kubeflow and other needed software on IBM Cloud Kubernetes Service (IKS).
- Use IKS to simplify the work of initializing a Kubernetes cluster on IBM Cloud.
- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
- Leverage other IBM Cloud services to extend an ML application.
28小时
课程概览
This instructor-led, live training in 上海 (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.

- By the end of this training, participants will be able to:
- Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
- Use OpenShift to simplify the work of initializing a Kubernetes cluster.
- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
- Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.
35小时
课程概览
This instructor-led, live training in 上海 (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this training, participants will be able to:

- Install and configure Kubeflow on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
- Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
- Run entire machine learning pipelines on diverse architectures and cloud environments.
- Using Kubeflow to spawn and manage Jupyter notebooks.
- Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
28小时
课程概览
This instructor-led, live training in 上海 (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this training, participants will be able to:

- Install and configure Kubeflow on premise and in the cloud.
- Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
- Run entire machine learning pipelines on diverse architectures and cloud environments.
- Using Kubeflow to spawn and manage Jupyter notebooks.
- Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
21小时
课程概览
This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.

By the end of this training, participants will be able to:

- Install and configure MLflow and related ML libraries and frameworks.
- Appreciate the importance of trackability, reproducability and deployability of an ML model
- Deploy ML models to different public clouds, platforms, or on-premise servers.
- Scale the ML deployment process to accommodate multiple users collaborating on a project.
- Set up a central registry to experiment with, reproduce, and deploy ML models.
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