北京市朝阳区现场MLOps培训

北京市朝阳区现场MLOps培训

在北京市朝阳区由讲师进行实时指导的MLOps本地培训课程。

北京 - Digital 01 Building

数码01大厦

北京 北京市朝阳区光华路丙12号 100000 ,
数码01大厦
在北京市朝阳区的培训中心学习MLOps。中国国际贸易中心与嘉里中心之间,背倚国贸中心,与嘉里公寓隔街相望,总建筑面积共32000平方米。 周边配套 商场:北京国贸商城旗舰店、万达新世界商场、贵友商场 银行:广发银行国贸支行、华夏银行京广支行、兴业银行光华路支行、华夏银行光华支行、中信银行财富中心支行 酒店:恋都商旅酒店、如家快捷酒店、建国饭店、万达索菲特大酒店、京伦饭店、北京伯豪瑞廷酒店、北京嘉里大酒店、千禧大酒店 餐饮:祖母的厨房、小王府、恒河印度餐厅、时时乐建国门餐厅等 查看更多

客户评论

★★★★★
★★★★★

MLOps子类别

北京市朝阳区现场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 北京市朝阳区 (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 北京市朝阳区 (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 北京市朝阳区 (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 北京市朝阳区 (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 北京市朝阳区 (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 北京市朝阳区 (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 北京市朝阳区 (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 北京市朝阳区 (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.
MLOps,北京市朝阳区, 小组MLOps课程在北京市朝阳区, 北京市朝阳区短期MLOps培训, 北京市朝阳区, MLOps, 北京市朝阳区MLOps周末培训, 北京市朝阳区MLOps课程, 学习MLOps在北京市朝阳区, 北京市朝阳区MLOps训练, 北京市朝阳区MLOps辅导班, 北京市朝阳区MLOps老师, 北京市朝阳区一对一MLOps课程, 北京市朝阳区MLOps晚上培训, 北京市朝阳区MLOps企业培训, 北京市朝阳区MLOps培训师, 学MLOps班在北京市朝阳区

促销课程

订阅促销课程

为尊重您的隐私,我公司不会把您的邮箱地址提供给任何人。您可以享有优先权和随时取消订阅的权利。

我们的客户

is growing fast!

We are looking to expand our presence in China!

As a Business Development Manager you will:

  • expand business in China
  • recruit local talent (sales, agents, trainers, consultants)
  • recruit local trainers and consultants

We offer:

  • Artificial Intelligence and Big Data systems to support your local operation
  • high-tech automation
  • continuously upgraded course catalogue and content
  • good fun in international team

If you are interested in running a high-tech, high-quality training and consulting business.

Apply now!

该网站在其他国家/地区