Docker for MLOps: End-to-End Pipeline Containerization Training Course

This live, instructor-led training in Mexico (available online or on-site) is tailored for engineers seeking to expand their Docker proficiency. The goal is to enable the deployment of applications on a larger scale while maintaining precise control.

Upon completion, participants will be capable of:

• Constructing their own Docker images.
• Deploying and managing a large number of Docker applications.
• Evaluating different container orchestration solutions to determine the most suitable one.
• Configuring a continuous integration process for Docker applications.
• Integrating Docker applications with established continuous integration processes.
• Enhancing the security of their Docker applications.

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.

Learn how to build, deploy, and administer containerized environments using Docker, Kubernetes, and OpenShift. This five-day, hands-on training covers container images, Kubernetes workloads, cluster networking, storage, security, monitoring, and practical OpenShift administration. Participants gain the skills needed to operate modern container platforms and troubleshoot applications across development and production environments.

In this instructor-led, live training in Mexico (onsite or remote), participants will learn how to create and manage Docker containers, followed by deploying a sample application inside a container. Participants will also learn how to automate, scale, and manage their containerized applications within a Kubernetes cluster. Furthermore, the training covers advanced topics, guiding participants through the process of securing, scaling, and monitoring a Kubernetes cluster.

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

• Set up and run a Docker container.
• Deploy a containerized server and web application.
• Build and manage Docker images.
• Set up a Docker and Kubernetes cluster.
• Use Kubernetes to deploy and manage a clustered web application.
• Secure, scale and monitor a Kubernetes cluster.

Course Objectives: Gain both theoretical and practical skills in Docker and Kubernetes.

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 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.