Online or onsite, instructor-led live MLOps training courses demonstrate through interactive hands-on practice how to use MLOps tools to automate and optimize the deployment and maintenance of ML systems in production.
MLOps training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. SanYa onsite live MLOps trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Hainan - Four Points by Sheraton
No.78, Sanya Bay Road, Sanya, Hainan, China, Sanya, China, 572000
The 5-star Fourpoints by Sheraton Hainan Sanya offers relaxing beach-side living with an outdoor pool and a wellness centre. ...
The 5-star Fourpoints by Sheraton Hainan Sanya offers relaxing beach-side living with an outdoor pool and a wellness centre. A 1-minute walk from the beach, the luxurious hotel features karaoke facilities and upscale rooms with a laptop safe box. On-site parking and Wi-Fi in public areas are free. Fourpoints by Sheraton is a 35-minute drive from Duty Free Shop or Sanya Railway Station and a 15-minute drive from Phoenix International Airport. It is a 10-minute drive from East Island and West Island. Tianya Haijiao is a 20-minute drive away, while the Guanyin Statue of Hainan is a 40-minute car journey away. All rooms are fitted with a flat-screen TV, a minibar and an iron. A writing desk and a seating area are also included. En suite bathrooms have both a bathtub and shower facilities. Guests can work out at the gym, play tennis on site, or check emails at the business centre. The tour desk can help guests organise cycling trips. There is a gift shop on site for last minute purchase. Japanese, Chinese and Western cuisine can be enjoyed at the hotel’s restaurants. We speak your language! Four Points by Sheraton Hainan, Sanya has been welcoming Booking.com guests since 7 Dec 2012 Hotel Rooms: 370, Hotel Chain: Four Points by Sheraton
This instructor-led, live training in SanYa (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.
This instructor-led, live training in SanYa (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.
This instructor-led, live training in SanYa (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.
This instructor-led, live training in SanYa (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.
This instructor-led, live training in SanYa (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.
This instructor-led, live training in SanYa (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.
This instructor-led, live training in (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.
This instructor-led, live training in SanYa (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.
This instructor-led, live training in SanYa (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.
Read more...
Last Updated:
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
Online MLOps training in SanYa, MLOps training courses in SanYa, Weekend MLOps courses in SanYa, Evening MLOps training in SanYa, MLOps instructor-led in SanYa, MLOps classes in SanYa, MLOps trainer in SanYa, MLOps one on one training in SanYa, MLOps on-site in SanYa, Evening MLOps courses in SanYa, MLOps instructor-led in SanYa, Online MLOps training in SanYa, MLOps private courses in SanYa, MLOps instructor in SanYa, MLOps coaching in SanYa, Weekend MLOps training in SanYa, MLOps boot camp in SanYa
