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

This instructor-led, live training in Mexico (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 Mexico (online or onsite) is designed for advanced-level platform engineers and DevOps professionals who want 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.
• 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 Mexico (available online or onsite) is designed for Kubernetes practitioners aiming to prepare for the CKS exam.

By the end of this session, participants will have the skills to secure Kubernetes environments and container-based applications throughout the entire application life cycle, from build and deployment to runtime.

This 7-day program offers a thorough, practical exploration of designing, deploying, and managing cloud-native applications using contemporary DevOps methodologies.

Attendees will learn how to architect scalable microservices systems, optimize container environments, and oversee production workloads with Kubernetes. The curriculum addresses advanced deployment techniques, GitOps-driven automation, and observability strategies to guarantee system reliability and performance.

Great emphasis is placed on addressing real-world operational hurdles, such as incident response, failure simulation, and root cause analysis. The program wraps up by leveraging AI-powered tools to aid in troubleshooting and speed up operational decision-making.

Upon completion of the training, participants will possess a clear grasp of how to build, deploy, monitor, and sustain resilient distributed systems within a Kubernetes environment.

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

Upon completion 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 Mexico—delivered online or onsite—is designed for developers and data scientists who aim to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow in on-premise and cloud settings.
• Create, deploy, and oversee ML workflows using Docker containers and Kubernetes.
• Run full machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Mexico (online or onsite) is aimed at beginner-level 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 Mexico (online or onsite) is designed for intermediate developers and DevOps engineers looking to integrate Minikube into 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 Mexico (online or onsite) is designed for engineers who wish to evaluate the approaches and tools available today to make informed decisions about adopting MLOps within their organizations.

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

• Install and configure various MLOps frameworks and tools.
• Build a skilled team capable of 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 required to build one.
• Experiment with different machine learning frameworks and servers for production deployment.
• Operationalize the entire Machine Learning process to ensure it is reproducible and maintainable.