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

This live, instructor-led training in Latvia (online or onsite) is tailored for advanced AI engineers and data scientists with intermediate-to-advanced experience. It aims to help participants enhance DeepSeek model performance, reduce latency, and deploy AI solutions efficiently by leveraging modern MLOps practices.

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

• Optimize DeepSeek models for improved efficiency, accuracy, and scalability.
• Implement established best practices for MLOps and model versioning.
• Deploy DeepSeek models on both cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions with greater effectiveness.

This live, instructor-led training in Latvia (online or onsite) is tailored for advanced platform engineers and DevOps professionals aiming to master application scaling using microservices and Kubernetes.

By the end of this program, 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 Latvia, available online or on-site, is designed for Kubernetes practitioners aiming to prepare for the CKS exam.

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

This seven-day intensive programme offers a practical, immersive experience in creating, releasing, and managing cloud-native applications through contemporary DevOps methodologies.

Learners will investigate the design of scalable microservice architectures, the optimisation of container ecosystems, and the administration of production workloads via Kubernetes. The curriculum encompasses sophisticated release mechanisms, GitOps-driven automation, and observability standards aimed at guaranteeing system stability and high performance.

Significant emphasis is placed on practical operational hurdles, such as incident management, failure simulation, and root cause investigation. The training culminates with the application of AI-enabled utilities to assist in fault-finding and enhance operational decision-making.

Upon completion, participants will possess a robust comprehension of constructing, deploying, monitoring, and sustaining resilient distributed systems within a Kubernetes-centric environment.

This instructor-led, live session in Latvia (conducted online or on-site) targets engineers seeking to utilize Helm to enhance the workflow for installing and managing Kubernetes applications.

Following this training, participants will be equipped to:

• Install and set up Helm.
• Create consistent builds for Kubernetes applications.
• Share applications using Helm charts.
• Deploy third-party applications stored as Helm charts.
• Control the release cycles of Helm packages.

This live, instructor-led training in Latvia (offered online or onsite) is designed for developers and data scientists aiming to build, deploy, and manage machine learning workflows on Kubernetes.

Once this training is complete, participants will have the ability to:

• Install and configure Kubeflow for both on-premise and cloud deployments.
• Construct, deploy, and handle ML workflows using Docker containers and Kubernetes.
• Run full machine learning pipelines across diverse architectures and cloud platforms.
• Use Kubeflow to initiate and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This live, instructor-led training in Latvia (available online or on-site) is designed for software developers and DevOps professionals at beginner to intermediate levels seeking to learn how to set up and manage a local Kubernetes environment with Minikube.

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

• Install and configure Minikube on their local machines.
• Understand the core 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 session, offered either online or on-site, is designed for intermediate developers and DevOps engineers aiming to adopt Minikube within their development processes.

By the conclusion of this training, participants will be capable of:

• Setting up and managing a local Kubernetes environment with Minikube.
• Understanding how to deploy, manage, and troubleshoot applications on Minikube.
• Incorporating Minikube into continuous integration and deployment pipelines.
• Optimizing development procedures by utilizing Minikube's advanced features.
• Adhering to best practices for local Kubernetes development.

This live, instructor-led training session in Latvia (available online or onsite) is tailored for engineers looking to evaluate contemporary tools and strategies, helping them make well-informed decisions regarding MLOps adoption in their organizations.

By the conclusion of this training, participants will gain the ability to:

• Set up and configure a range of MLOps frameworks and tools.
• Build teams with appropriate skills for the construction and support of MLOps systems.
• Handle, verify, and version data for application in ML models.
• Grasp the components of an ML Pipeline and the necessary tools for its creation.
• Test various machine learning frameworks and servers for production deployment.
• Operationalize the full Machine Learning process to ensure it remains reproducible and maintainable.