Online MLOps Training in New Zealand

This instructor-led, live training in New Zealand (online or on-site) is designed for advanced-level AI engineers and data scientists with intermediate to advanced experience who aim to enhance DeepSeek model performance, minimise latency, and deploy AI solutions efficiently using modern MLOps practices.

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

• Optimise DeepSeek models for efficiency, accuracy, and scalability.
• Implement best practices for MLOps and model versioning.
• Deploy DeepSeek models on cloud and on-premises infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

MLOps on Kubernetes is a framework for automating the training, validation, packaging, and deployment of machine learning models using containerised pipelines and GitOps workflows.

This instructor-led, live training (online or on-site) is aimed at intermediate-level practitioners who wish to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be equipped 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-led presentations and technical deep dives.
• Hands-on exercises that build real-world CI/CD workflows.
• Live-lab practice deploying ML workloads to Kubernetes.

Course Customisation Options

• Organisations 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.

Course Format

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

Course Customisation Options

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

Docker is a containerisation platform used to build reproducible, portable, and scalable environments for ML systems.

This instructor-led, live training (online or on-site) is designed for intermediate to advanced-level technical professionals who wish to containerise and operationalise complete ML pipelines using Docker.

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

• Containerise ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines using Docker and supporting tools.
• Implement versioning, reproducibility, and CI/CD for ML components.
• Deploy, monitor, and scale ML services in containerised environments.

Course Format

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

Course Customisation Options

• For customised training aligned with specific ML infrastructure needs, please contact us to discuss options.

This instructor-led, live training in New Zealand (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organisation.

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

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right skills for 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 needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalise the entire Machine Learning process so that it's reproducible and maintainable.