NobleProg delivers specialized MLOps training opportunities within the vibrant landscape of Bhutan. Tailored to the unique professional needs of this Himalayan nation, our courses empower local businesses and individuals with cutting-edge skills in MLOps. Participants benefit from expert-led instruction designed to drive innovation and sustainable growth in Bhutan's evolving economy.
Available both online and on-site, these instructor-led live MLOps training courses offer interactive, hands-on experiences that demonstrate how to leverage MLOps tools to automate and optimize the deployment and upkeep of ML systems within production environments.
You can choose between 'online live training' or 'onsite live training' for MLOps. Online sessions (also referred to as 'remote live training') are conducted via an interactive remote desktop. Onsite live training can be delivered directly at your location in Bhutan or at NobleProg's corporate training centers in Bhutan.
NobleProg -- Your Local Training Provider
Bhutan, Thimphu - Classroom
near Le Méridien , Chorten Lam, Thimphu, Bhutan, 11001
Set in Thimphu, this classroom is well located in Chorten Lam with all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
Bhutan, Paro - Classroom
near Le Méridien Riverfront, thimphu hwy, Shaba, Paro, Bhutan, 12001
Set in Paro, this classroom is well located near Paro-Thimphu Highway around 4 km from the airport, and 7 km from Rinpung Dzong, and possess all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
This instructor-led, live training in Bhutan (online or on-site) is aimed at advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices.
By the end of this training, participants will be able to:
Optimize DeepSeek models for efficiency, accuracy, and scalability.
Implement best practices for MLOps and model versioning.
Deploy DeepSeek models on cloud and on-premise infrastructure.
Monitor, maintain, and scale AI solutions effectively.
This live training on Bhutan supports intermediate practitioners in constructing automated MLOps pipelines on Kubernetes. Learners will design CI/CD workflows, apply GitOps strategies, and deploy ML models via containerized infrastructure to achieve scalable and reproducible machine learning operations.
This practical training in Bhutan empowers you with the capabilities to build, train, and serve machine learning models on Kubernetes using Kubeflow. You will learn to navigate the ecosystem, design scalable pipelines, and oversee production-ready workloads with industry best practices.
This instructor-led course in Bhutan walks professionals through the process of containerizing complete ML pipelines with Docker. Learners will gain expertise in creating reproducible environments, orchestrating training and inference tasks, and setting up CI/CD for scalable MLOps deployments.
This instructor-led, live training in Bhutan (online or onsite) is designed for developers and data scientists aiming to build, deploy, and manage machine learning workflows on Kubernetes.
By the conclusion of this training, participants will be able to:
Install and configure Kubeflow in on-premise and cloud environments.
Build, deploy, and manage ML workflows using Docker containers and Kubernetes.
Run entire machine learning pipelines across diverse architectures and cloud environments.
Use Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
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