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

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

• Creating custom Docker images.
• Deploying and managing a large number of Docker applications.
• Evaluating various container orchestration solutions to select the most appropriate one.
• Establishing a continuous integration process for Docker applications.
• Integrating Docker applications with existing continuous integration toolchains.
• Implementing security measures 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 Uzbekistan (online or onsite) is designed for advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and efficiently deploy AI solutions 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 Uzbekistan (available online or onsite) is designed for advanced-level platform engineers and DevOps professionals who aim to master application scaling 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.
• Utilise Helm charts for efficient service deployment.
• Monitor and maintain the health of microservices in production environments.
• Apply best practices for security and compliance within a Kubernetes environment.

This live training, conducted by an instructor in Uzbekistan (either online or onsite), targets Kubernetes practitioners aiming to prepare for the CKS exam.

By the conclusion of this training, participants will be equipped to secure Kubernetes environments and container-based applications throughout the entire application life cycle, covering build, deployment, and runtime stages.

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

By the end of the session, participants will be equipped to:

• Install and configure Kubeflow in on-premise and cloud environments.
• Construct, deploy, and oversee ML workflows based on Docker containers and Kubernetes.
• Execute full machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to launch and control Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This guided, live training in Uzbekistan (online or on-site) is tailored for software developers and DevOps practitioners at beginner to intermediate levels who aim to master the setup and management of a local Kubernetes environment via Minikube.

Upon completing this training, participants will be equipped to:

• Install and configure Minikube on their local workstations.
• Grasp the fundamental concepts and architectural structure of Kubernetes.
• Deploy and oversee containers utilizing kubectl and the Minikube dashboard.
• Implement persistent storage and networking configurations for Kubernetes.
• Leverage Minikube for application development, testing, and debugging.

This instructor-led live training in Uzbekistan (online or onsite) is designed for intermediate-level developers and DevOps engineers who want 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 in Uzbekistan (online or on-site) is tailored for engineers seeking to evaluate current approaches and tools to make informed decisions about adopting 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 right team with the necessary skills to build 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 required to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process to ensure it is reproducible and maintainable.