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

This live, instructor-led training on Italy (delivered online or onsite) is designed for engineers seeking to expand their Docker proficiency. It focuses on deploying applications at scale while ensuring robust control and management.

By the end of the program, participants will be able to:

• Create proprietary Docker images.
• Manage the deployment of extensive Docker application portfolios.
• Evaluate and select the optimal container orchestration solution.
• Implement continuous integration processes for Docker applications.
• Seamlessly integrate Docker applications with existing continuous tooling.
• Fortify the security of their Docker applications.

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

• Create custom Docker images.
• Deploy and manage a high volume of Docker applications.
• Assess various container orchestration solutions and select the most appropriate one.
• Establish a continuous integration workflow for Docker applications.
• Integrate Docker applications into existing continuous integration toolchains.
• Enhance the security of Docker applications.
• Utilize Kubernetes to deploy and manage diverse environments within a single cluster.
• Secure, scale, and monitor a Kubernetes cluster.

This instructor-led, live training in Italy (online or onsite) is designed for advanced-level 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.

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 both cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

Acquire the skills to build, deploy, and administer containerized environments utilizing Docker, Kubernetes, and OpenShift. This five-day, hands-on training encompasses container images, Kubernetes workloads, cluster networking, storage, security, monitoring, and practical OpenShift administration. Participants will gain the necessary competencies to operate modern container platforms and troubleshoot applications across both development and production environments.

In this instructor-led, live training in Italy (onsite or remote), participants will learn how to create and manage Docker containers, then deploy a sample application inside a container. Participants will also learn how to automate, scale, and manage their containerized applications within a Kubernetes cluster. Finally, the training goes on to more advanced topics, walking 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.

Training Objectives: Acquire theoretical and practical skills in Docker and Kubernetes.

This live, instructor-led training in Italy (delivered online or onsite) targets developers and data scientists who aim to build, deploy, and manage machine learning workflows on Kubernetes.

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

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

This instructor-led, live training in Italy (online or onsite) is designed for engineers who want to assess the current approaches and tools available, enabling them 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 team with the appropriate skills to construct 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 production deployment.
• Operationalize the entire Machine Learning process to ensure it is reproducible and maintainable.