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

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

• Creating custom Docker images.
• Deploying and managing a high volume of Docker applications.
• Evaluating various container orchestration platforms and selecting the most appropriate solution.
• Establishing a continuous integration pipeline for Docker applications.
• Integrating Docker applications into existing CI/CD toolchains.
• Implementing security best practices for Docker applications.
• Utilizing Kubernetes to deploy and manage diverse environments within a single cluster.
• Securing, scaling, and monitoring a Kubernetes cluster.

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

This instructor-led, live training in Turkey (online or onsite) is aimed at advanced-level platform engineers and DevOps professionals who wish to master scaling applications using microservices and Kubernetes.

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

• Design and implement scalable microservices architectures.
• Deploy and manage applications on Kubernetes clusters.
• Utilize Helm charts for efficient service deployment.
• Monitor and maintain the health of microservices in production.
• Apply best practices for security and compliance in a Kubernetes environment.

This instructor-led, live training in Turkey (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 protect Kubernetes environments and container-based applications throughout all phases of the application life cycle: build, deployment, and runtime.

This instructor-led, live training in Turkey (online or onsite) is designed for engineers looking to streamline the installation and management of Kubernetes applications using Helm.

Upon completing this training, participants will be equipped to:

• Install and configure Helm.
• Build reproducible Kubernetes applications.
• Distribute applications as Helm charts.
• Execute third-party applications packaged as Helm charts.
• Manage Helm package releases.

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

By the end of the session, participants will be capable of:

• Installing and configuring Kubeflow in both on-premise and cloud settings.
• Constructing, deploying, and managing ML workflows based on Docker containers and Kubernetes.
• Running full machine learning pipelines across diverse architectures and cloud environments.
• Using Kubeflow to spawn and control Jupyter notebooks.
• Building ML training, hyperparameter tuning, and serving workloads across multiple platforms.

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

Upon completing this training, participants will be able to:

• Install and configure Minikube on their local machines.
• Grasp the fundamental concepts and architecture of Kubernetes.
• Deploy and manage containers using kubectl and the Minikube dashboard.
• Establish persistent storage and networking solutions for Kubernetes.
• Use Minikube for developing, testing, and debugging applications.

This instructor-led, live training in Turkey (online or onsite) is designed for intermediate-level developers and DevOps engineers who want to incorporate Minikube into their development workflows.

Upon completing this training, participants will be able to:

• Set up and manage a local Kubernetes environment using Minikube.
• Deploy, manage, and debug applications on Minikube.
• Integrate Minikube into their continuous integration and deployment (CI/CD) pipelines.
• Enhance their development process by leveraging Minikube's advanced features.
• Apply best practices for local Kubernetes development.

This instructor-led, live training in Turkey (online or onsite) targets 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.