MLOps Training in Bratislava

This instructor-led, live training in Bratislava (online or on-site) 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 looking to build automated and scalable MLOps pipelines on Kubernetes.

Upon completion of this training, participants will be able to:

• Design end-to-end CI/CD pipelines tailored 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:

• 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 containerization platform designed to create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led training, available both online and onsite, targets intermediate to advanced technical professionals aiming to containerize and operationalize complete ML pipelines using Docker.

After completing this training, participants will be capable of:

• Containerizing ML workloads for training, validation, and inference.
• Designing and orchestrating end-to-end ML pipelines with Docker and related tools.
• Implementing versioning, reproducibility, and CI/CD processes for ML components.
• Deploying, monitoring, and scaling ML services within containerized environments.

Course Format

• Interactive lectures complemented by practical demonstrations.
• Hands-on exercises centered on constructing real-world ML pipeline components.
• Live-lab implementation for end-to-end containerized workflows.

Course Customization Options

This instructor-led live training in Bratislava (online or onsite) targets engineers who wish to evaluate current approaches and tools. This evaluation supports intelligent decision-making regarding the adoption of MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools.
• Assemble a team with the appropriate 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 is reproducible and maintainable.