MLOps Training in Athens

This instructor-led, live training in Athens (online or onsite) is designed for advanced AI engineers and data scientists with intermediate-to-advanced experience. The course aims to help participants enhance DeepSeek model performance, minimize latency, and efficiently deploy AI solutions using modern MLOps practices.

Upon completion 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, targets intermediate-level practitioners aiming to construct automated, scalable MLOps pipelines on Kubernetes.

Upon completion, participants will be capable of:

• Designing end-to-end CI/CD pipelines for machine learning.
• Implementing GitOps workflows for model deployment and versioning.
• Automating the training, testing, and packaging of ML models.
• Integrating 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 containerization platform designed to construct reproducible, portable, and scalable environments for machine learning systems.

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

By the end of this training, participants will be capable of:

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

Course Format

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

Course Customization Options

This instructor-led live training in Athens (online or on-site) is designed for engineers who wish to evaluate available approaches and tools to make informed decisions regarding the adoption of MLOps within their organizations.

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

• Install and configure various MLOps frameworks and tools.
• Assemble the appropriate team with the necessary 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.