MLOps Training in Prague

This instructor-led, live training in Prague (online or onsite) is designed for advanced AI engineers and data scientists with intermediate-to-advanced expertise who aim to improve DeepSeek model performance, reduce latency, and deploy AI solutions efficiently using contemporary MLOps practices.

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

• Optimize DeepSeek models for efficiency, accuracy, and scalability.
• Apply best practices for MLOps and model versioning.
• Deploy DeepSeek models on both cloud and on-premise infrastructure.
• Effectively monitor, maintain, and scale AI solutions.

MLOps on Kubernetes serves as a framework designed to automate the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This live training, conducted by an instructor (available online or onsite), targets intermediate-level practitioners looking to construct automated and scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be able to:

• Architect end-to-end CI/CD pipelines for machine learning.
• Implement GitOps workflows to manage model deployment and versioning.
• Automate the processes of training, testing, and packaging ML models.
• Integrate monitoring, alerting, and rollback strategies.

Format of the Course

• Instructor-led 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 that aligns 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 live training, available online or on-site, targets technical professionals at intermediate to advanced levels who aim to containerize and operationalize full machine learning pipelines using Docker.

Upon completing this training, participants will be equipped to:

• Containerize ML workloads covering training, validation, and inference.
• Design and orchestrate end-to-end ML pipelines using Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

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

Customization Options

• For training tailored to specific ML infrastructure requirements, please contact us to explore customization options.

This instructor-led, live training in Prague (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.