江苏省现场MLOps培训

江苏省现场MLOps培训

在江苏省由讲师进行实时指导的MLOps本地培训课程。

苏州 - 晋合广场

11th Floor, No. 2 Jinhe Plaza,

Suzhou 88 Huachi Street, Industrial Park 215021 ,
晋合广场
在江苏省的培训中心学习MLOps。The center of Jinhe Plaza is located at the core of Suzhou Industrial Zone, on the east bank of Jinji Lake. Jinhe Plaza is a comprehensive building, including high-end shopping malls, five-star hotels, international Grade A office buildings, and Regus is located on the eleventh floor of the building. As the most famous landmark in Suzhou Financial Center, with its advanced facilities and central location, it has attracted many Fortune 500 companies such as Nokia, Philips and 3M. Jinhe Plaza... 查看更多

南京 - 金陵亚太商务酒店

金陵饭店亚太商务楼

南京, Jiangsu 南京市鼓楼区新街口汉中路2号 8楼 210005 ,
南京-金陵亚太商务酒店
在江苏省的培训中心学习MLOps。金陵饭店亚太商务楼位于市中心的繁华地段——鼓楼区新街口商务中心区。雷格斯商务中心就在这幢商业新地标的第8层。亚太商务楼不仅设有甲级办公楼层,还配套建设了五星级酒店、行政酒廊、饭店和SPA水疗中心。亚太商务楼因其发达的轨道交通条件(地铁1号线和2号线新街口站)和地处主要商业区而颇受IBM、惠普、花旗银行、德勤及西门子等跨国企业的青睐。南京地处东南,是江苏省省会,有十朝古都之誉,因其宝贵的古代文化遗产和壮丽景色而闻名,现已成为中国最繁荣的大都市之一。 查看更多

苏州 - 晋合广场

晋合广场

苏州市, Jiangsu 工业园区 华池街 88号 晋合广场 2号 11楼 215021 ,
 苏州 - 晋合广场
在江苏省的培训中心学习MLOps。雷格斯晋合广场中心位于苏州工业区核心、金鸡湖东岸。晋合广场是一座综合型大楼,包括高档购物中心、五星级酒店、国际甲级办公楼等,而雷格斯位于大厦十一层。作为苏州金融中心最著名的地标性建筑,凭借其先进的设施和中心地理位置,吸引了众多世界500强公司,如诺基亚、飞利浦和3M等。 晋合广场可方便通往苏州各大核心区域。地铁1号线时代广场站位于大厦的地下层,而距离通往上海和南京的高铁站仅需5分钟车程 查看更多

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MLOps子类别

江苏省现场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 江苏省 (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 江苏省 (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 江苏省 (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 江苏省 (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 江苏省 (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 江苏省 (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 江苏省 (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 江苏省 (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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