Online MLOps Training in Bulgaria

This instructor-led, live training in Bulgaria (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 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 looking to construct automated, scalable MLOps pipelines on Kubernetes.

Upon completion of this training, 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 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 live training, available online or on-site, targets technical professionals at intermediate to advanced levels who aim to containerize and operationalize complete ML pipelines using Docker.

Upon completing this course, participants will be able to:

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

Course Format

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

Course Customization Options

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

This instructor-led live training, conducted in Bulgaria (online or onsite), is designed for engineers who wish to evaluate current approaches and tools. It aims to support intelligent decision-making on how to proceed with adopting MLOps in their organizations.

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 to construct and support 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.