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

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.

This instructor-led, live training in Italy (online or onsite) targets advanced-level platform engineers and DevOps professionals who wish 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 live, instructor-led training in Italy (online or onsite) is designed for Kubernetes practitioners seeking to prepare for the CKS exam.

Upon completion, participants will understand how to secure Kubernetes environments and container-based applications throughout the application lifecycle, covering the build, deployment, and runtime stages.

This instructor-led, live training in Italy (online or onsite) is designed for engineers looking to leverage 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 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 tailored for software developers and DevOps professionals at beginner to intermediate levels who want 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 Italy (online or onsite) is designed for intermediate developers and DevOps engineers aiming to utilize Minikube within 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 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.