珠海现场MLOps培训

珠海现场MLOps培训

在珠海由讲师进行实时指导的MLOps本地培训课程。

珠海 - 天朗海峰

天朗海峰2栋1210

珠海 香洲区 519000 ,
珠海天朗海峰
在珠海的培训中心学习MLOps。天朗海峰位于前山桥西、珠海大道和南屏次干道交叉的东南角,容积率:5.2,占地面积17428㎡,总建筑面积:130977.32㎡(其中住宅约70955.03㎡,商业约19702.17㎡,地下室约33012.6㎡,其它约4407.76㎡),建筑高度达185m。 天朗海峰功能布置:三层地下室(停车场、人防等功能配置); 四层裙楼商场;裙楼顶层以上2幢住宅塔楼,每幢塔楼58层(其中:塔楼1层为住宅入户大堂,2层为会所,3层为商业配套的办公楼层,第22层、第37层为避难层,其余的53个楼层均为住宅标准层) 建筑风格: 完整干净的住宅塔楼,结构体系合理,平面方整没有开口切槽,极富原创性; 城市地标、沉静内敛,却又挺拔峻峭。 有玉树临风的气质,没有表象性的性态模拟,从性格上贴近滨海花园城市的风范 公园:回归公园,竹仙洞公园 超市:得一超市 商业:华发商都,滨河风情酒吧街、南屏海鲜街、南屏市场等 学校:容闳国际幼儿园、容闳小学、北山小学、南屏中学 查看更多

珠海 - 钰海环球金融中心

12层

珠海, Guangdong 香洲区吉大九洲大道中1009 ,
钰海环球金融中心
在珠海的培训中心学习MLOps。雷格斯金融中心大厦位于香洲区吉大九洲大道中,坐落于珠海市吉大CBD核心区内,紧邻区政府。珠海九洲直升机机场距雷格斯中心仅7分钟车程,且只需13分钟车程即可到达珠海火车站。作为经济特区的珠海通往香港和澳门十分便捷。该中心周边拥有各商务设施,如银行、宾馆、高档购物商场和餐馆。酒店如假日酒店、珠海德翰酒店均在五分钟步行距离内。风景名胜,如珠海市博物馆、石花山公园和白莲洞公园均临近中心。对于正在寻求优质办公空间以及专业服务的国内外企业而言,雷格斯珠海钰海环球金融中心将会是价格合理的最佳选择。 查看 珠海 的所有办公地点 查看更多

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MLOps子类别

珠海现场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 珠海 (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 珠海 (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 珠海 (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 珠海 (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 珠海 (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 珠海 (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 珠海 (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 珠海 (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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