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. Onsite live MLOps trainings in Leiden can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Leiden
Golden Tulip/Tulip Inn Leiden Centre, Schipholweg 3, Leiden, Netherlands, 2316XB
The 4-star Golden Tulip Leiden Center is located right next to the Tulip Inn Leiden Center hotel; together they accommodate 7 stars under one roof. The hotels are ideally located with Leiden Central Station directly opposite and many motorways within easy reach. A beautiful location in the center of the historic city of Leiden full of museums and picturesque spots. Experience, enjoy and be inspired.
Golden Tulip & Tulip Inn Leiden Center have a large diversity of hotel rooms; from comfortable and modern furnished standard rooms to luxurious executive rooms. In total there are 155 rooms. Furthermore, the hotels have a renovated restaurant called "Rubens", a cozy bar / lounge and an extensive meeting & conference center.
The 6 multifunctional rooms make Golden Tulip & Tulip Inn Leiden Center the ideal location for small and medium-sized meetings. The rooms have a capacity of 4 to 100 people. The meeting rooms offer countless possibilities in terms of setup and technical facilities. Convenience, efficiency, hospitality and professionalism are paramount. All rooms have air conditioning, free wireless internet and daylight.
Until the early 19th century, fishing in Noordwijk aan Zee remained the most important form of income. Later the population started to focus more and more on tourism. More than 1 million overnight stays take place every year. Noordwijk aan Zee consists of several districts and is located on the dunes. There are various catering establishments such as hotels, entertainment centers and restaurants (also on the beach) and there is a shopping center that has a Sunday opening. Every year events take place on the boulevard around the lighthouse on the Vuurtorenplein. The municipality has a KNRM rescue station and a reformed church (1647) with pulpit from the 17th century.
Since the merger with the Noordwijkerhout municipality, the municipality consists of four centers.
Noordwijk aan Zee, traditionally a fishing village, has become a seaside resort with a long coastal strip of approximately 13 km. . Noordwijk aan Zee has two boulevards, both of which are named after a queen, Queen Wilhelmina Boulevard and Queen Astrid Boulevard.
Noordwijk-Binnen radiates the tranquility of earlier centuries in the old core. In 1992 the old village center was designated as a protected village view under the Monuments Act.
Noordwijkerhout, a village and former municipality northeast of Noordwijk..Noordwijk aan Zee is rated as the 12th richest location in the Netherlands. Beer magnate Freddy Heineken has built a villa there with the characteristic green roof.
The Two Brothers Noordwijk Beach Hotel is located on the boulevard of Noordwijk with a view of the village and the sea and has several conference rooms and training rooms that make it the perfect place for business meetings and stimulating training. Noordwijk is a 30-minute drive from Amsterdam and The Hague and only 20 minutes from Leiden.
This instructor-led, live training in Leiden (online or onsite) 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 instructor-led, live training in Leiden (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 Leiden (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 Leiden (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 Leiden (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 (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.
This instructor-led, live training in Leiden (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 (online or onsite) is aimed at machine learning engineers who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices.
By the end of this training, participants will be able to:
Build reproducible workflows and machine learning models.
Manage the machine learning lifecycle.
Track and report model version history, assets, and more.
Deploy production ready machine learning models anywhere.
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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 TM
Course - Kubeflow
Provisional Upcoming Courses (Contact Us For More Information)
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