福建现场MLOps培训

福建现场MLOps培训

在福建由讲师进行实时指导的MLOps本地培训课程。

厦门 - 国际银行大厦

国际银行大厦中心8楼

厦门市, Fujian 国际银行大厦中心8楼,思明区鹭江道8号 361001 ,
Xiamen International Plaza
在福建的培训中心学习MLOps。国际银行大厦是地处厦门CBD鹭江路上拥有37层楼的地标性建筑物。面临大海且厦门著名风景点鼓浪屿即在对岸。鹭江路已成为“厦门外滩”及一个著名的度假胜地。由于地理位置优越,众多不同行业客户包括金融投资、专业服务、物流、进出口、电子类公司都入驻大楼。附近的设施包括中国银行、中国工商银行、厦门海景千禧大酒店及厦门温德姆酒店。 中心交通便利,集海路、陆路和航空便捷于一体。渡轮码头只有几分钟距离之远。附近有快速公交站,乘坐快速公交去厦门火车站只有15分钟时间。高速列车可以从厦门到上海、南京、合肥、福州、南昌和鹰潭 查看更多

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MLOps子类别

福建现场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 福建 (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 福建 (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 福建 (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 福建 (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 福建 (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 福建 (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 福建 (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 福建 (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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