Delivered either online or on-site, these instructor-led live MLOps training courses utilize interactive, hands-on exercises to demonstrate how to leverage MLOps tools for automating and optimizing the deployment and maintenance of ML systems in production environments.
MLOps training is offered in two formats: "online live training" and "onsite live training." Online live training (also known as "remote live training") is conducted via an interactive remote desktop. Onsite live training can be conducted locally at customer premises in Copenhagen or within NobleProg corporate training centers in Copenhagen.
You can also take bus 26 or 7A and get off around Vesterbros Torv; from there it is a short walk.
By bus
Useful nearby stops include:
Vesterbros Torv (Vesterbrogade) — about 1 minute from the area.
Frederiksberg Allé (Vesterbrogade) — nearby.
Trommesalen (Vesterbrogade) — nearby.
Bus 26 and 7A serve the Vesterbrogade corridor.
København, Copenhagen
NobleProg København, Bredgade 37, København K, denmark, 1260
Near Amalienborg Palace & Royal Theatre.
If you arrive by train
If you arrive at Copenhagen Central Station (København H):
Walk to København H Metro.
Take the M3 or M4 metro toward Kongens Nytorv.
Get off at Kongens Nytorv.
Walk north toward Nyhavn, then continue along Bredgade.
Bredgade 37 is about 500 m / 6 minutes' walk from Kongens Nytorv.
Another easy option
Marmorkirken (Marble Church) Metro Station is also very close to Bredgade 37. The nearby Bredgade area is about 2 minutes' walk from Marmorkirken station.
By bus
There are bus stops around Dronningens Tværgade / Bredgade, so you can also travel by bus and walk the final few minutes.
This instructor-led live training, offered in Copenhagen (online or on-site), is tailored for advanced AI engineers and data scientists with intermediate-to-advanced experience who seek to enhance DeepSeek model performance, minimise latency, and deploy AI solutions efficiently using modern MLOps practices.
By the end of the training, participants will be able to:
Optimise DeepSeek models for improved efficiency, accuracy, and scalability.
Apply best practices for MLOps and model versioning.
Deploy DeepSeek models on cloud and on-premise infrastructure.
Monitor, maintain, and scale AI solutions with confidence.
This live training in Copenhagen supports intermediate practitioners in building automated MLOps pipelines on Kubernetes. Learners will design CI/CD workflows, implement GitOps strategies, and deploy ML models using containerized infrastructure to achieve scalable and reproducible machine learning operations.
This hands-on training in Copenhagen provides the necessary skills to build, train, and serve machine learning models on Kubernetes using Kubeflow. You will learn to navigate the ecosystem, author scalable pipelines, and manage production-ready workloads following best practices.
This instructor-led training in Copenhagen walks professionals through the process of containerizing complete ML pipelines with Docker. Participants will gain the skills to build reproducible environments, orchestrate training and inference workloads, and implement CI/CD for scalable MLOps deployments.
This live, instructor-led training in Copenhagen, offered either online or on-site, is designed for developers and data scientists seeking 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 in both on-premise and cloud-based settings.
Design, deploy, and manage ML workflows utilizing Docker containers and Kubernetes.
Execute complete machine learning pipelines across varied architectures and cloud environments.
Leverage Kubeflow to create and manage Jupyter notebooks.
Develop ML training, hyperparameter tuning, and serving workloads that span multiple platforms.
This instructor-led, live training in Copenhagen (online or onsite) is designed for engineers looking to evaluate current approaches and tools. It empowers participants to make intelligent decisions about 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 production deployment.
Operationalize the entire Machine Learning process so that it is reproducible and maintainable.
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