Whether conducted online or onsite, instructor-led MLOps training courses illustrate, through interactive hands-on exercises, how to leverage MLOps tools to automate and optimize the deployment and maintenance of ML systems in a production environment.
MLOps training is offered in two formats: 'online live training' and 'onsite live training'. Online live training (also known as 'remote live training') is delivered via an interactive remote desktop. Onsite live training can be conducted locally at the customer's premises in Nepal or at NobleProg's corporate training centres 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.
MLOps on Kubernetes provides a framework for automating the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.
This instructor-led live training, available online or onsite, targets intermediate-level practitioners looking to build automated, scalable MLOps pipelines on Kubernetes.
Upon completion of this training, participants will be able to:
Design end-to-end CI/CD pipelines for machine learning.
Implement GitOps workflows for model deployment and versioning.
Automate the training, testing, and packaging of ML models.
Integrate monitoring, alerting, and rollback strategies.
Course Format
Instructor-guided presentations and technical deep dives.
Hands-on exercises focused on building real-world CI/CD workflows.
Live-lab practice for deploying ML workloads to Kubernetes.
Customization Options
Organizations can request tailored content aligned with their internal MLOps tools and infrastructure.
Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.
Upon completion of this training, attendees will gain the skills to:
Explore the Kubeflow ecosystem and its core components.
Develop reproducible workflows using Kubeflow Pipelines.
Execute scalable training jobs on Kubernetes.
Efficiently serve machine learning models via Kubeflow Serving.
Course Format
Guided presentations coupled with collaborative discussions.
Hands-on labs utilizing real Kubeflow components.
Practical exercises to construct end-to-end ML workflows.
Course Customization Options
Tailored versions of this training can be arranged to align with your team’s technology stack and project requirements.
Docker serves as a containerization platform designed to create reproducible, portable, and scalable environments for machine learning systems.
This instructor-led live training, available both online and onsite, targets intermediate to advanced technical professionals who aim to containerize and operationalize comprehensive ML pipelines using Docker.
After completing this training, participants will be equipped to:
Containerize ML training, validation, and inference workloads.
Design and orchestrate end-to-end ML pipelines utilizing Docker and complementary tools.
Implement versioning, reproducibility, and CI/CD processes for ML components.
Deploy, monitor, and scale ML services within containerized environments.
Format of the Course
Interactive lectures accompanied by practical demonstrations.
Hands-on exercises centered on constructing real-world ML pipeline components.
Live-lab implementation for end-to-end containerized workflows.
Course Customization Options
For customized training tailored to specific ML infrastructure requirements, please contact us to discuss available options.
Online MLOps training in Nepal, MLOps training courses in Nepal, Weekend MLOps courses in Nepal, Evening MLOps training in Nepal, MLOps instructor-led in Nepal, MLOps coaching in Nepal, Evening MLOps courses in Nepal, MLOps instructor-led in Nepal, MLOps instructor in Nepal, Online MLOps training in Nepal, MLOps classes in Nepal, MLOps trainer in Nepal, MLOps one on one training in Nepal, MLOps on-site in Nepal, MLOps private courses in Nepal, MLOps boot camp in Nepal, Weekend MLOps training in Nepal
