NobleProg delivers expert-led MLOps training courses across Nepal, empowering professionals and organizations with the latest industry skills. Leveraging our extensive presence in the Indian subcontinent, we provide accessible and high-quality learning solutions tailored to the local market. Participants gain valuable insights through interactive sessions designed to enhance career growth and organizational performance.
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 Nepal or at NobleProg's corporate training centers in Nepal.
NobleProg -- Your Local Training Provider
Nepal, Kathmandu - Classroom
near Soaltee, Tahachal Marg, Kathmandu, Nepal, 44600
Set in Kathmandu, this classroom is well located near Tahachal Marg 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.
Nepal, Thamel, KTM - Classroom
near Radisson , Ward 2, Kathmandu, Nepal, 44600
Set in Kathmandu, this classroom is well located near Thamel, 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.
This instructor-led, live training in Nepal (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 Nepal 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 Nepal 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 Nepal 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 Nepal (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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