MLOps on Kubernetes: CI/CD Pipelines for Machine Learning Training Course

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

• Create custom Docker images.
• Deploy and manage a large volume of Docker applications.
• Evaluate various container orchestration solutions and select the most appropriate one.
• Establish a continuous integration process for Docker applications.
• Integrate Docker applications with existing CI tooling workflows.
• Implement security measures for Docker applications.
• Utilize Kubernetes to deploy and manage multiple environments within a single cluster.
• Secure, scale, and monitor a Kubernetes cluster.

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.

This instructor-led, live training in Bulgaria (online or onsite) is designed for advanced-level platform engineers and DevOps professionals who want to master the techniques for scaling applications using microservices and Kubernetes.

Upon completion of this training, participants will be capable of:

• Designing and implementing scalable microservices architectures.
• Deploying and managing applications on Kubernetes clusters.
• Utilizing Helm charts for efficient service deployment.
• Monitoring and maintaining the health of microservices in production.
• Applying best practices for security and compliance within a Kubernetes environment.

This instructor-led, live training in Bulgaria, offered online or onsite, is designed for Kubernetes practitioners aiming to prepare for the CKS exam.

By the end of this course, participants will understand how to secure Kubernetes environments and container-based applications throughout the various stages of an application's life cycle: build, deployment, and runtime.

This instructor-led, live training in Bulgaria (online or onsite) is aimed at engineers who wish to use Helm to streamline the process of installing and managing Kubernetes applications.

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

• Install and configure Helm.
• Create reproducible builds of Kubernetes applications.
• Share applications as Helm charts.
• Run third-party applications saved as Helm charts.
• Manage releases of Helm packages.

This live, instructor-led training in Bulgaria, offered online or on-site, is designed for developers and data scientists seeking to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this session, participants will gain the capability to:

• Install and configure Kubeflow in on-premises and cloud settings.
• Develop, deploy, and control ML workflows leveraging Docker containers and Kubernetes.
• Operate full machine learning pipelines across diverse architectures and cloud environments.
• Utilize Kubeflow to create and manage Jupyter notebooks.
• Establish ML training, hyperparameter tuning, and serving workloads across various platforms.

This instructor-led, live training in Bulgaria (online or onsite) is designed for beginner to intermediate software developers and DevOps professionals who want to learn how to set up and manage a local Kubernetes environment using Minikube.

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

• Install and configure Minikube on their local machine.
• Understand the basic concepts and architecture of Kubernetes.
• Deploy and manage containers using kubectl and the Minikube dashboard.
• Set up persistent storage and networking solutions for Kubernetes.
• Utilize Minikube for developing, testing, and debugging applications.

This instructor-led, live training in Bulgaria (online or on-site) is designed for intermediate-level developers and DevOps engineers who intend to utilize Minikube as part of their development workflow.

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

• Set up and manage a local Kubernetes environment using Minikube.
• Understand how to deploy, manage, and debug applications on Minikube.
• Integrate Minikube into their continuous integration and deployment pipelines.
• Optimize their development process using Minikube's advanced features.
• Apply best practices for local Kubernetes development.

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.