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. Nijmegen onsite live MLOps trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Nijmegen
Fifty Two Degrees, Jonkerbosplein 52, Nijmegen, Netherlands, 6534 AB
Located in FiftyTwoDegrees, a striking black tower of 18 floors with a 'kink' in the top that is at an angle and known as an unrivaled business address in the east of the Netherlands. FiftyTwoDegrees is designed as a unique business, cultural and social center and is a combination of high concentration of knowledge companies and well thought-out offices that are grouped around a central square
Wageningen
Wageningen WUR, Stippeneng 2 , Wageningen, netherlands, 6708 WE
Wageningen is located on the banks of the Lower Rhine. Excavations date to the first settlements in this area around the Stone Age. The hills to the east of the city offered protection from floods of the Rhine from the Bronze Age. The city has a turbulent history and has been occupied or destroyed several times. The Rhine also changed course in 1421, moved further south and therefore had an adverse effect on the Wageningen trade. In the 17th century, the city started growing tobacco and there were several cigar manufacturers. The floodplains of the Rhine to the south also had several brickworks, one of which can still be seen.
In 1876, the Dutch government decided to build the first agricultural school in Wageningen because it was in the heart of the country and surrounded by a wide variety of soils. Since then, the city has grown enormously and Wageningen University is now a world-famous Life Sciences university. Wageningen also has an important inland port.
The halls of Impulse
Centrally located on Wageningen Campus, near the De halen van Impulse. The glass building and the cheerful colors provide a welcoming atmosphere. Impulse is an excellent location for not too large symposia or training courses. Impulse is located in building 115 on the Campus.
Address Stippeneng 2
6708 WE Wageningen
Wageningen Campus is indicated at the main roads of Wageningen. Follow the P-route to P3 on the campus. The route is indicated from all large parking spaces to the individual buildings on the campus. Impulse's building number is 115.
Arnhem
Arnhem Park Tower, Nieuwe stationsstraat 20, Arnhem, Netherlands, 6811 KS
The Arnhem Nijmegen region is known as a conference destination and has a thriving food and health sector, thanks in part to the presence of two universities.
The Park Tower is located in a modern building on top of the train station in the center of Arnhem's business district. The railway connects the city to national and international cities. Moreover, Arnhem is close to the German border and is easily accessible due to the many train routes. From the 13th and 14th floors of the city center you have a view over the city and the World Trade Center is right next door.
This instructor-led, live training in Nijmegen (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 Nijmegen (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 Nijmegen (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 Nijmegen (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 Nijmegen (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 Nijmegen (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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