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

This instructor-led live training in Kenya (online or on-site) is designed for advanced AI engineers and data scientists with intermediate-to-advanced experience who aim to improve DeepSeek model performance, reduce latency, and efficiently deploy AI solutions using contemporary MLOps practices.

By the conclusion 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 Kenya (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 Kenya (available online or onsite) is designed for Kubernetes practitioners aiming to prepare for the CKS exam.

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

This intensive 7-day programme offers a comprehensive, practical journey into the design, deployment, and operation of cloud-native applications through modern DevOps practices.

Participants will discover how to architect scalable microservices, optimise container environments, and manage production workloads using Kubernetes. The curriculum addresses advanced deployment techniques, GitOps-driven automation, and observability strategies to guarantee system reliability and high performance.

A significant emphasis is placed on real-world operational challenges, such as incident response, failure simulation, and root cause analysis. The programme wraps up with the integration of AI-powered tools designed to streamline troubleshooting and expedite operational decision-making.

Upon completion, participants will possess a solid grasp of how to construct, deploy, monitor, and sustain resilient distributed systems within a Kubernetes ecosystem.

This instructor-led, live training in Kenya (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 instructor-led, live training in Kenya, delivered online or onsite, targets developers and data scientists seeking to construct, deploy, and maintain machine learning workflows on Kubernetes.

By the conclusion of this training, participants will be equipped to:

• Install and configure Kubeflow in on-premise and cloud environments.
• Build, deploy, and manage ML workflows leveraging Docker containers and Kubernetes.
• Operate entire machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This guided, live training in Kenya (online or in-person) is designed for beginner to intermediate-level software developers and DevOps professionals who wish 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 Kenya (online or onsite) is aimed at intermediate-level developers and DevOps engineers who wish to use Minikube as a 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 Kenya (online or onsite) is aimed at 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.