MLOps Training in Gaia

This instructor-led live training in Gaia (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.

MLOps on Kubernetes provides a framework to automate the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led live training, available online or onsite, targets intermediate-level practitioners looking to build automated and scalable MLOps pipelines on Kubernetes.

Upon completion, participants will be able to:

• Design end-to-end CI/CD pipelines for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises that build real-world CI/CD workflows.
• Live-lab practice deploying ML workloads to Kubernetes.

Course Customization Options

• Organizations may request tailored content aligned with their internal MLOps tools and infrastructure.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

Docker serves as a containerization platform designed to build reproducible, portable, and scalable environments for machine learning systems.

This instructor-led, live training, available online or onsite, targets intermediate to advanced technical professionals seeking to containerize and operationalize complete ML pipelines using Docker.

Upon completing this training, participants will be able to:

• Containerize ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines using Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

• Interactive lectures supported by practical demonstrations.
• Hands-on exercises focused on constructing real-world ML pipeline components.
• Live-lab implementation for end-to-end containerized workflows.

Course Customization Options

• For customized training tailored to specific ML infrastructure requirements, please contact us to discuss available options.

This instructor-led, live training in Gaia (online or onsite) is designed for engineers looking to evaluate current approaches and tools. The course helps participants make informed decisions about adopting MLOps within their organizations.

Upon completion, 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 consumption by ML models.
• Understand the components of an ML Pipeline and the necessary tools to construct one.
• Experiment with different machine learning frameworks and servers for production deployment.
• Operationalize the entire Machine Learning lifecycle to ensure it is reproducible and maintainable.