MLOps Training in Lyon

This instructor-led, live training in Lyon (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.

MLOps on Kubernetes serves as 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 aiming to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing 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 that build real-world CI/CD workflows.
• Live-lab practice deploying ML workloads to Kubernetes.

Course Customization Options

• Organizations may 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 within a Kubernetes environment.
• Deploy machine learning models efficiently via Kubeflow Serving.

Format of the Course also allows for the evaluation of participants.

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

Docker serves as a robust containerization platform, enabling the creation of reproducible, portable, and scalable environments tailored for machine learning systems.

This live, instructor-led training, available online or on-site, is designed for intermediate to advanced technical professionals seeking to containerize and operationalize comprehensive ML pipelines using Docker.

By the end of this program, participants will be equipped to:

• Encapsulate ML training, validation, and inference workloads within containers.
• Architect and orchestrate full-cycle ML pipelines leveraging Docker and associated tools.
• Establish versioning, reproducibility, and CI/CD workflows for ML components.
• Deploy, oversee, and scale ML services within containerized infrastructure.

Course Structure

• Engaging lectures complemented by practical demonstrations.
• Practical exercises centered on developing authentic ML pipeline elements.
• Real-time lab sessions implementing end-to-end containerized workflows.

Customization Options

• For tailored training aligned with specific ML infrastructure requirements, please reach out to us to explore available options.

This guided live training in Lyon, offered 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 the session, participants will be equipped to:

• Install and configure Kubeflow in on-premise and cloud environments.
• Construct, deploy, and manage ML workflows using Docker containers and Kubernetes.
• Execute full machine learning pipelines across diverse architectures and cloud ecosystems.
• Utilize Kubeflow to launch and manage Jupyter notebooks.
• Create ML training, hyperparameter tuning, and serving workloads across various platforms.

This instructor-led, live training in Lyon (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.