Whether delivered online or on-site, these instructor-led live MLOps training courses provide interactive, hands-on experiences that demonstrate how to leverage MLOps tools to streamline and optimize the deployment and upkeep of machine learning systems in production environments.
MLOps training is offered as either "online live training" or "onsite live training". Online live training (also known as "remote live training") is conducted via an interactive remote desktop. Onsite live training can take place directly at client premises in Lille or at NobleProg corporate training centers in Lille.
NobleProg – Your Local Training Provider
Lille, Gare Flandres
NobleProg Lille, 21 Avenue le Corbusier, Lille, france, 59800
In front of Flandres TGV Train Station
From Lille train stations
From Lille-Flandres: the address is essentially right next to the station — about 1–3 minutes on foot.
From Lille-Europe: walk toward Lille-Flandres along Avenue Le Corbusier. It is roughly 5–10 minutes on foot.
So you don't need a bus or metro if you're arriving by train.
If you're coming by bus
The nearest bus stops to Avenue Le Corbusier include Lion d'Or and Jacquet. Several local lines serve the area, including 13, 86, L5 and L91.
This instructor-led, live training in Lille (online or onsite) targets advanced AI engineers and data scientists with intermediate-to-advanced expertise who aim to boost DeepSeek model performance, reduce latency, and efficiently deploy AI solutions using contemporary MLOps practices.
By the conclusion of this training, participants will be capable of:
Optimizing DeepSeek models for efficiency, accuracy, and scalability.
Applying best practices for MLOps and model versioning.
Deploying DeepSeek models across cloud and on-premise infrastructure.
Effectively monitoring, maintaining, and scaling AI solutions.
This live training on Lille enables intermediate practitioners to construct automated MLOps pipelines on Kubernetes. Participants will learn to design CI/CD workflows, implement GitOps strategies, and deploy ML models using containerized infrastructure to achieve scalable and reproducible machine learning operations.
This practical training in Lille 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 by adhering to industry best practices.
This instructor-led course in Lille walks professionals through the process of containerizing entire ML pipelines with Docker. Participants will learn to build reproducible environments, manage training and inference workloads, and implement CI/CD for scalable MLOps deployments.
This live, instructor-led training, delivered in Lille (either online or onsite), is designed for developers and data scientists looking to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this session, participants will be capable of:
Installing and configuring Kubeflow in on-premise and cloud environments.
Building, deploying, and managing ML workflows that rely on Docker containers and Kubernetes.
Running full machine learning pipelines across diverse architectures and cloud platforms.
Using Kubeflow to create and manage Jupyter notebooks.
Developing ML training, hyperparameter tuning, and serving workloads across various platforms.
This instructor-led, live training in Lille (online or onsite) is designed for engineers who wish to evaluate the current approaches and tools available, helping them make informed decisions about the future adoption of MLOps within their organizations.
Upon completing 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 reproducible and maintainable.
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