MLOps Training in Sud-Vest

This instructor-led, live training in Sud-Vest (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.

MLOps on Kubernetes provides 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) is designed for intermediate-level practitioners seeking to build automated, scalable MLOps pipelines on Kubernetes.

Upon completion of 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 focused on building real-world CI/CD workflows.
• Live-lab practice for 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:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• 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 that enables the creation of reproducible, portable, and scalable environments tailored for machine learning systems.

This live, instructor-led training—available either online or onsite—is designed for technical professionals with intermediate to advanced expertise who are looking 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 end-to-end ML pipelines leveraging Docker and complementary tools.
• Establish versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized infrastructures.

Course Format

• Interactive lectures complemented by practical demonstrations.
• Hands-on exercises dedicated to constructing real-world ML pipeline components.
• Live laboratory sessions for implementing end-to-end containerized workflows.

Customization Options

• To tailor the training to specific ML infrastructure requirements, please reach out to discuss potential customizations.

This live, instructor-led training, available in Sud-Vest (online or on-site), targets developers and data scientists looking to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this session, participants will be able to:

• Install and configure Kubeflow in on-premise and cloud settings.
• Construct, deploy, and oversee ML workflows using Docker containers and Kubernetes.
• Run full machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to create and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Sud-Vest (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.